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ESCUELA TÉCNICA SUPERIOR DE INGENIERÍAS AGRARIAS
DEPARTAMENTO DE INGENIERÍA AGRÍCOLA Y FORESTAL
TESIS DOCTORAL:
ESTUDIO DE VARIEDADES MINORITARIAS DE VID
(Vitis vinifera L.): DESCRIPCIÓN, CARACTERIZACIÓN
AGRONÓMICA Y ENOLÓGICA DE MATERIAL PROCEDENTE
DE LAS ISLAS BALEARES
STUDY OF MINOR GRAPEVINE CULTIVARS
(Vitis vinifera L.): DESCRIPTION, AGRONOMIC AND
OENOLOGICAL CHARACTERIZATION OF VARIETIES FROM
THE BALEARIC ISLANDS
Presentada por Sonia García Muñoz para optar al grado de
doctora por la Universidad de Valladolid
Dirigida por:
Félix Cabello Sáenz de Santa María
Agradecimientos:
Esta tesis se ha llevado a cabo gracias a una beca predoctoral de Formación de Personal
Investigador otorgada a Sonia García Muñoz (Beca predoctoral del Instituto Nacional de
Investigación y Tecnología Agraria y Alimentaria, INIA, proyecto RTA 04 175-C3-3
“Caracterización y selección de variedades de vid de Baleares”). Sin ella la realización de este
trabajo hubiera sido prácticamente imposible. Gracias a Julián Barrera, Jose Antonio Gómez-
Limón y a Manuel Gómez Pallarés por facilitarme los trámites administrativos.
Me gustaría dar las gracias a Félix Cabello, mi director de tesis, por darme la oportunidad de
entrar en el mundo de la investigación, por confiar en mí desde el principio, por guiarme a lo largo
de la elaboración de este trabajo y por enseñarme gran parte de lo que he aprendido sobre el
patrimonio vitícola español. Reconozco el gran esfuerzo y sacrificio que has tenido que hacer
para dirigir esta tesis, sólo espero que haya merecido la pena.
Gracias al personal del IMIDRA, especialmente a todos los que cuidan de la colección de
variedades de vid Alberto, Enrique, Lucio, Javi, Conchi, Toñi...Gracias a Alejandro Benito, Laura
Gaforio y Gregorio Muñoz, sin cuyo apoyo este trabajo no hubiera salido adelante, a María
Teresa de Andrés por sus enseñanzas sobre genética, a Miguel Ángel por facilitar el trabajo en
bodega. Gracias Mariano Cabellos, Marta Ruiz, María José Marqués, Ramón Bienes, Jesús
Alegre he aprendido mucho con vosotros. Gracias a Ermi y a Lola, por preparar esos platos tan
ricos!! Mi reconocimiento a la labor de todos los becarios (Pre-carios), gracias a su esfuerzo se
lleva a cabo gran parte del trabajo de investigación en el IMIDRA, ellos dignifican este trabajo.
Gracias por el apoyo que he recibido, especialmente del grupo más naranja: Alicia, Elena, Emi,
Estela, Inma, Irene, Jesús, Laura, Luis y Pablo, juntos hemos compartido problemas (y
soluciones). Espero que la vida os sonría.
Gracias a la Universidad de las Islas Baleares, y al IRFAP, Govern Balear, especialmente a
Antoni Martorell Nicolau por todas sus enseñanzas sobre la vida y cultura de las Islas Baleares.
Gracias a todo el personal de las bibliotecas por las que he pasado buscando información
sobre variedades minoritarias: biblioteca Nacional de Madrid, Biblioteca Nazionale di Firenze,
Biblioteca Amigos del País y biblioteca Pública de Palma, Biblioteca Profesor Dalmasso, Vassal…
Gracias a los que me han ayudado con las traducciones: Mimar, Gaizka, Violaine, Luis, Rosa,
Gabriella…
Gracias a todos con los que he compartido mi vida nómada:
Nella bella Italia: un ringraziamento particolare a CRA - Centro di Ricerca per l'Enologia di Asti,
per come mi hanno ricevuto. Grazie a Nadia, ad Andriani per la sua pazienza in laboratorio, grazie
a Daniela Borsa per tutto il lavoro fatto con gli aromi dei vitigni minori delle Baleari. Grazie a tutto il
precariato: Enrico, Giovanna, Loretta, Maurizio, Max, Marieta, Silvia, Domenico (e Valentina)...
avete reso il mio soggiorno in Italia molto più piacevole. Grazie ad Anna Schneider (Istituto di
Virologia Vegetale - Grugliasco - Torino) per avermi trasmesso l'amore per i vitigni minori italiani.
Grazie alla "mia famiglia italiana" Gabriella, Gino (e Isabella), grazie per aver contribuito alla mia
conoscenza dell'Italia, della cucina italiana, delle grappe, dei vini e per la vostra generosità. Laura,
non dimenticherò mai i tuoi gnocchi, sono i migliori del mondo!!!!
En France: Merci à l'INRA-Montpellier et à l'INRA Domaine de Vassal de m'avoir si bien
accueillie. Je tiens à remercier spécialement Valérie et Thierry pour les discussions qui ont enrichi
ce travail. De tout cœur, je remercie tout particulièrement Thierry Lacombe sans qui ce travail
n'aurait pu être possible; pour son expertise en ampélographie et pour ses encouragements pour
la dernière ligne droite de l'écriture de cette thèse.
In USA: Thanks to University of California and Smart´s Lab for receiving me. Special thank to
Dave, Mimar and Teena, it was great work with you!! Gracias a Adriana, Elena, Mimar, Mónica y
Pepe por hacerme la vida más fácil en California.
En Alcalá de Henares: gracias a Alicia, Laura, Luda, Marta, Naiara, Patricia, Sonia…por
acogerme en vuestras casas (y en vuestras vidas), lo hemos pasado bien juntas!!
En Palencia: gracias a Ana, Cristo, Álvaro y Muño por vuestro apoyo, por aguantarme estos
últimos días, por los vinitos y por esas charlas tan agradables que hemos compartido hablando de
de las cosas de la vida.
Josu G. Alday-ri, estatistikarekin eta lan espezializatuekin laguntzeagatik, nire berrikusle
partikularra izateagatik, zientziaz nola idatzi irakasteagatik, urte guzti hauetan nire ondoan
izateagatik eta izan duzun pazientziarengatik. Zuri esker burutu ahal izan dut lan hau. Nire
ezkongabe bizitzan zure falta sentitzen dudalako, nire begietako ninia zu zarelako. Mila esker!!
A mis amigas de toda la vida, Alicia, Lidia, Mariam, Mila, Myriam, Noemí, Sonia, Rebeca,
gracias por estar, por apoyar, por reír, por preguntar, por responder…Gracias también a Jorge,
Sergio, Raúl, Marian, Chema, Sergio, Toni, Javi (los “et al”) que cuidan de ellas, porque juntos
hacemos un grupo especial: soy vuestra fan number one!!
En Portugalete (Bizkaia): a mi “familia postiza”, Yolanda, Jesús, Celia y a mis “amigos
postizos”: Mila Esker!!
Gracias a mi Familia, por todo el apoyo prestado en todas las cosas que he emprendido a lo
largo de mi existencia y por seguir por mis andaduras por el mundo. Gracias hermanita por ser
como eres y por compartirlo todo conmigo desde el estado embrionario. Gracias papá, gracias
mamá, gracias Quique, Elena, Rebeca y Javi, gracias a los más pequeños de la familia, a la
fantástica Nerea, al bueno de Gabriel, a la risueña Inés y al simpático de Martín, que con su
espontaneidad hacen que por un momento la vida sea más fácil.
Gracias a todos los que posiblemente me he olvidado!!
Gracias a todos, porque lo que soy ahora lo he aprendido de todos vosotros.
“Todo lo que aprendemos en nuestras breves vidas no es más que una pizca insustancial arrancada de la enormidad de lo que nunca sabremos”
(Montero R. 2008. Instrucciones para salvar el mundo. Madrid, Spain: Alfaguara)
Photographs: All photos are from “LaTiAniceta” copyrighted and all rights reserved ®
Contents
Abstract - Resumen 1
Chapter 1 Introduction 3
Chapter 2 Evidence of loss in cultivated grapevines diversity: The example
of the Balearic Islands (Spain)
11
Chapter 3 Ampelography: an old technique with future uses, the case of
minor varieties of Vitis vinifera L. from the Balearic Islands
33
Chapter 4 Grape varieties (Vitis vinifera L.) from the Balearic Islands:
genetic characterization and relationship with Iberian Peninsula
and Mediterranean Basin
55
Chapter 5 Evaluation of susceptibility to powdery mildew (Erysiphe
necator) in Vitis vinifera varieties
79
Chapter 6 Aromatic characterization and oenological potential of 21 minor
varieties (Vitis vinifera L.)
85
Chapter 7 Sensory characterization and factors influencing quality of wines
made from 18 minor varieties (Vitis vinifera L.)
105
Chapter 8 Synthesis 131
Chapter 9 Conclusions - Conclusiones 139
Appendix I Vegetal material 143
Appendix II Ampelographic descriptions and agronomic characterization 145
Appendix III Grape aroma data 171
Appendix IV Spanish abstract 179
Contenidos
Abstract - Resumen 1
Capítulo 1 Introducción 3
Capítulo 2 Evidencias de la pérdida de diversidad de vides cultivadas: el
ejemplo de las Islas Baleares (España)
11
Capítulo 3 Ampelografía: una vieja técnica con usos futuros, el caso de
variedades minoritarias de vid (Vitis vinifera L.) de las Islas
Baleares
33
Capítulo 4 Variedades de vid (Vitis vinifera L.) de las Islas Baleares:
caracterización genética y relaciones con la Península Ibérica y
la cuenca Mediterránea
55
Capítulo 5 Evaluación de la susceptibilidad al oídio (Erysiphe necator) en
variedades de Vitis vinifera
79
Capítulo 6 Caracterización aromática y potencial enológico de 21
variedades minoritarias de vid (Vitis vinifera L.)
85
Capítulo 7 Caracterización sensorial y factores que influyen en la calidad
de los vinos elaborados a partir de 18 variedades minoritarias
de vid (Vitis vinifera L.)
105
Capítulo 8 Síntesis 131
Capítulo 9 Conclusions - Conclusiones 139
Apéndice I Material vegetal 143
Apéndice II Descripciones ampelográficas y caracterización agronómica 145
Apéndice III Datos de aroma de uvas 171
Apéndice IV Resumen en castellano 179
Abstract
- 1 -
Abstract
The global oenological market is driving a cultivated grapevine homogenization around the word.
The loss of cultivars (Vitis vinifera L.) is an important problem in most of the viticulture areas that
must be stopped to conserve the varietal heritage.
The oenological market is changing, since nowadays the wine consumers are demanding new
wines styles, based on the originality, quality, and link to the “terroir” and with historical
background. Therefore it is necessary to satisfy these new requirements. The minor varieties,
most of them under risk of extinction, could be a great option to satisfy this demand, since these
varieties are perfectly adapted to the local environmental conditions. However the oenological
potential of most of these cultivars is unknown.
In this thesis, it has been studied a group of minor grapevine cultivars collected from 1914 to
2000 mainly in the Balearic Islands (Spain) and nowadays preserved in the Vitis Germplasm Bank
VGB “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain). A preliminary step to offer new
grapevine material to vinegrowers and winemakers is to identify perfectly the cultivars, with that
purpose ampelographic and microsatellite analysis were used. The historical links between the
cultivars and the Balearics Islands have been analyzed. It is also essential for promoting a new
cultivar to know the agronomical behaviour, including resistance to disease as powdery mildew
(Erysiphe necator Schwein.), and the oenological potential, based on aromatic grape
characterization and wine sensorial analysis. All of these parameters have been analysed in this
study.
In general, the studied cultivars showed great agronomical behaviour and most of them also
showed a high resistance to powdery mildew. The genetic analysis has pointed out that two gene
pools exist. The oenological potential of the minor varieties has been proved, since most of them
were well accepted by wine experts in sensorial analysis. The knowledge of the oenological
potential of the minor varieties could be one of the last opportunities that minor varieties have to
survive in the future.
Resumen
- 2 -
Resumen
La globalización del mercado enológico está conduciendo a una homogeneización de las
variedades de vid cultivadas alrededor del mundo. La pérdida de variedades (Vitis vinifera L.) es
un problema importante en la mayoría de las áreas vitícolas, este hecho debe detenerse para
conservar el patrimonio varietal.
El mercado enológico está en constante movimiento, actualmente los consumidores están
demandando nuevos estilos de vino basados en la originalidad y calidad, otra tendencia es el
consumo de productos de proximidad, ligados al territorio, que representen los antecedentes
históricos del mismo. Por lo tanto, es necesario satisfacer estos nuevos requerimientos. Las
variedades minoritarias, la mayoría en peligro de extinción, pueden ser una buena opción para
satisfacer esta nueva demanda, ya que estas variedades se encuentran perfectamente adaptadas
a las condiciones locales y medioambientales. Sin embargo, el potencial enológico de estas
variedades sigue siendo desconocido.
En esta tesis se ha estudiado un grupo de variedades minoritarias de vid recogidas entre los
años 1914 y 2000 principalmente en las Islas Baleares (España) y conservadas en la actualidad
en el Banco de Germoplasma de Vid BGV “Finca El Encín” (IMIDRA, Alcalá de Henares, Madrid,
España). Un paso preliminar para poder ofrecer a los viticultores y enólogos nuevo material
vitícola es identificar perfectamente a las variedades, para ello se han realizado descripciones
ampelográficas y análisis genéticos basados en el uso de marcadores moleculares. A su vez, se
ha analizado la relación histórica entre las variedades y las Islas Baleares. Otro aspecto
fundamental a la hora de promover nuevas variedades de vid es conocer el comportamiento
agronómico, incluyendo la resistencia a enfermedades como el oídio (Erysiphe necator Schwein.),
y el potencial enológico, basado en la caracterización aromática de las uvas y el análisis sensorial
de los vinos realizados. Todos estos parámetros se han analizado en este trabajo.
En general, las variedades estudiadas mostraron un buen comportamiento agronómico.
Además la gran mayoría mostró una alta resistencia al oídio. El análisis genético ha indicado la
existencia de dos reservas genéticas. Las variedades minoritarias han mostrado un alto potencial
enológico, al ser aceptados la mayoría de sus vinos por los expertos en el análisis sensorial. El
conocimiento del potencial enológico de las variedades minoritarias puede ser una de las últimas
oportunidades que tienen estas variedades para sobrevivir en el futuro.
Chapter 1
General view of Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain)
Introduction
Chapter 1
- 3 -
Introduction
“I am writing this booklet to spread the most important knowledge about the plague that destroys
all the vineyard in Europe and to do that these knowledge arrive to the heart of the viticulture
villages in this province, for all understands the grave danger of which are threatened and you
know the reserved place in the great battle for self-defense that, with no doubt, we will have to
fight. The agriculture, the industry and the trade are founds of richness of this province. Death of
the grapevine is the collapse of the viticultural country, it is not only the decadence, it is an
obstacle for the prosperity along a very extend period of time” (Pou y Bonet 1880). These words
were written to avoid the entry of the phylloxera in the Balearic Islands (Spain). Unfortunately, it
was not enough because the phylloxera plague appears in 1891 destroying much of the vineyard
in the Balearic Islands (Ballester 1911). However, more than a century later, the lost of the
grapevine growing area and grapevine diversity is a current problem in most of the viticulture
areas in the world including the Balearic Islands.
Based on DNA profiling results, there are around 5,000 grapevine varieties (Vitis vinifera L.;
This et al. 2006), however evidences of grapevine diversity loosing have been appointed (Bessis
2007; Carimi et al. 2010). Actually, international varieties, as Cabernet-Sauvignon, Sauvignon
blanc or Chardonnay, are present in most of the European countries as a consequence of wine
globalization market. Hence a few varieties are increasing the vineyard area worldwide, reducing
drastically the local cultivars and consequently the gene pool. Nowadays, the Spanish viticulture
area is divided on 73 Designations of Origin (DO) and includes 250 different cultivars in its
national grapevine catalogue, being the fourth country in grapevine diversity in the European
Union behind Italy, Portugal and France (Lacombe et al. 2011). Despite of the high number of
autochthonous cultivars the international varieties are widely spread and their cultivation are
allowed in most of the Spanish DO. In contrast, most of the minor varieties that could be called
“autochthonous” are not included in any Designations of Origin (Cabello 2004). In fact, the 95% of
the Spanish vineyard area is cultivated with only 34 cultivars; as a consequence, the local cultivars
have decreased over the years especially in their natural environmental conditions (This et al.
2006; Cipriani et al. 2010). These problems are also present in the Balearic Islands where the loss
of cultivars remains unknown.
The introduction of grapevine foreign material induces important changes in the local viticulture,
especially in isolated areas which are most sensitive to changes. In fact, some of the ancient
cultivars sited in the Balearic Islands are not found in natural conditions nowadays, fortunately
they are conserved in grapegene repositories. Actually, only four varieties (Callet, Manto Negro,
Pensal Blanca and Fogoneu) out of 20 allowed in the two out of the 73 Spanish Designations of
Origin located in the Balearic Islands are local varieties, the rest are internationally spread as
Introduction
- 4 -
Cabernet-Sauvignon, Tempranillo or Chardonnay. Therefore, the reduction of the number of
cultivars has caused the forgotten of the old cultivars.
The Balearic Islands, located in the Western part of the Mediterranean basin, are composed by
two of the 10 greatest islands in the Mediterranean Sea. Their wines were well known in the world
for its high quality since Roman time (Hidalgo 2002), wining several international competitions
during nineteen century. The Balearic viticulture has changed over the years; first greenfly named
“animaló” appeared (Haltica ampeloghaga Guer.; Salvator de Austria 1869), then the powdery
mildew (Erysiphe necator Schwein) arrived to the islands in 1851 (Salvator de Austria 1869;
Ballester 1911), and after that the phylloxera crisis (Hernández Robredo 1903), producing
changes in the local cultivars to satisfy the new demand of the consumers.
Due to the water barriers, the vegetal material exchange in the Balearic Islands is only possible
by sea, mostly with countries sited around the Mediterranean basin. Therefore, the current gene
pool is likely result from plant material exchanges in ports around the Mediterranean basin and
from natural crosses occurring on the islands (Prentice et al. 2003). As a consequence, the
strategic geographic location of the Balearic Islands and its isolation could be used to disentangle
the grapevine movements around occidental Mediterranean basin and the origin of some Balearic
cultivars.
The islands isolation has promoted high levels of endemic and genetic divergence (Filippetti et
al. 2005; Zerolo and Cabello 2006), which must be clear candidates for conservation (Wilson et al.
2009). Fortunately, new prospecting of grapevine material are rescuing varieties under risk of
extinction (Boursiquot et al. 2009; Santana et al. 2010), preserving them in grapevine collections
to prevent genetic erosion (This et al. 2006). As a consequence, these old varieties have to be
well identified. However, the lack of knowledge of ampelographic descriptions, the grape growing
characteristics of antique varieties and grapevine trade as well as the adaptation or change in the
name of the grapevines (Cipriani et al. 2010) could have caused the appearance of homonyms
and synonyms (Aradhya et al. 2003) implying that sometimes it is not possible to identify the
cultivars found in the new prospection (Sladonja et al 2007; Santana et al. 2010). Most of the
times, the varieties found in the new prospection are not registered in an official catalogue, thus
limiting the opportunity for their conservation, because the growing of these cultivars is not allowed
(Salmaso et al. 2008). Nevertheless, some of these varieties show really interesting properties for
wine production (Vilanova and Martínez 2007).
The oenological market is a dynamic process that needs to be always adapted to changes and
demands on wine market (Bertuccioli 2010), since the wine consumers taste and preferences
have changed during the last few years (Lesschaeve 2007). Nowadays, the wine consumers are
looking for a new sensorial experience, therefore original wine varieties are starting to be in
demand. Therefore, the interest in understanding the origin and genetic diversity of the germplasm
Chapter 1
- 5 -
rescued in different geographical areas is growing (Cipriani et al. 2010; Schneider et al. 2010).
However the potential of most of these wine varieties is unknown. Designations of Origin are
required unique and high quality wine varieties which should be linked historically to a geographic
area (Meredith et al. 1999; Santiago et al. 2008). At the same time, DOs play an important role in
food and wine marketing strategies (Douglas et al. 2001; Skuras and Vakrou 2002), since they are
based not only in the geographic area but also in wines quality and originality. Nowadays,
Designations of Origin are looking for wine varieties (Vitis vinifera L) linked to sites
(“authoctonous”), which could provide original and high quality wines, with the aim to increase
market possibilities (Bertuccioli 2010). Thus the use of minor varieties could be strong candidates
to fill this gap and to satisfy DO requirements, being also one of the last opportunities that minor
varieties have to survive in the future. The knowledge of minor varieties possibilities in the
changing oenological market is an urgent requirement.
In this study, we analyze 32 accessions of Vitis vinifera L. corresponding to 21 different
varieties of Vitis vinifera L. since some accessions or clones of the same variety were studied
(Appendix I). The vegetal material was collected from 1914 to 2000 mainly in the Balearic Islands
and nowadays the grapevines are preserved in the Vitis Germplasm Bank VGB “Finca El Encín”
(IMIDRA, Alcalá de Henares, Spain; Figure 1). However, based on antique bibliography, four
accessions of Beba variety were collected from Levante area and Girona, and Pampolat girat
variety from Tarragona.
Figure 1. Geographic location on the Western part of the Mediterranean Basin of the Balearic Islands, where the grapevine were mainly collected (circle), and location of the Vitis Germplasm Bank VGB “Finca El
Encín” (IMIDRA, Alcalá de Henares, Spain) where the grapevines have been preserved (black square)
The main objective of this study was to identify the possibilities of these varieties in the
oenological market. Synthesis of historical references, ampelographic descriptions, microsatellite
analysis, aromatic grapes characterization and sensory analysis of the made wines have been
used for this purpose.
Introduction
- 6 -
Main objectives
1.- To clearly identify the cultivars found in the antique bibliography providing further information
about the grapevines grown and lost in the Balearic Islands from XVII century to nowadays.
Chapter 2.
2.- To evaluate the ampelographic descriptions, genetic analysis, agronomic characterization,
must variables and phenology of minor grapevine accessions from the Balearic Islands. Chapter
3, Chapter 4 and Appendix II.
3.- To characterize the local cultivars growing in the Balearic Islands using microsatellite analysis,
and specifically (i) to clarify cases of synonyms and homonyms of these cultivars with varieties
cultivated in other countries; (ii) attempt to know the geographic movement and migration of these
varieties, and finally (iii) to establish the genetic relationship between them. Chapter 2, Chapter 3
and Chapter 4.
4.- To analyse the susceptibility of 159 cultivars of Vitis vinifera to powdery mildew (Erysiphe
necator Schwein.) including the cultivars collected in the Balearic Islands. Chapter 5.
5.- To analyse the aromatic potential of 21 grapevines, mainly native from the Balearic Islands.
Chapter 6.
6.- To characterize wines made with local cultivars using sensorial description. Chapter 7.
It is important to identify the relationship between cultivar and site to offer to wine consumers
minor varieties linked with the “terroir” and with historical background. In this way, antique
bibliography is key to understand the evolution of the viticulture in a specific area, relative to the
number of cultivars used and growing area. At the same time, this information provides the
possibility to correctly identify the varieties found in the new prospection and the grapevine
cultivars loss though time.
Other essential point is to provide to vinegrowers and winemakers a well identified material. In
grapevine, ampelography is the preliminary method for the clarification of vegetable material
(Schneider et al. 2008) and its combination with microsatellite markers allows the correct
identification of cultivars (Lopes et al. 1999; Schneider et al. 2010). Microsatellites markers are a
powerful tool to distinguish cultivars, clarifying synonyms and homonyms (Lopes et al. 1999;
Laucou et al. 2011), and to establish genetic relationships (Boursiquot et al. 2009; Vargas et al.
2009). Chloroplast microsatellite markers are also useful to approach the geographic origin of the
grapevine cultivars (Arroyo-García et al. 2006; Imazio et al. 2006) which is usually difficult to
establish due to the high material exchange (This et al. 2006).
Finally, agronomic behaviour, the resistance to powder mildew, oenological potential of minor
cultivars as well as the acceptance of the wines by experts are required characteristics in order to
Chapter 1
- 7 -
know the possibilities of minor varieties in the changing oenological market. Typicality of the wines
is influenced by a large number of factors, underlining grapevine and vintage (Maitre et al. 2010).
Volatile compounds in grapes are influenced by environmental conditions (Ribéreau-Gayon et al.
2006) and they are important as a source of flavour in wines (Franco et al. 2004). In this study all
of these factors related to final wine quality are analysed.
It hopes that the information provided in this research would lead to improve the quality of the
wines and help to gain more interest of autochthonous varieties, guaranteeing the survival of
some of these minor varieties.
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Chapter 2
La mancanza di descrizioni e notizie sopra molti vitigni coltivati anche in regioni viticole
importanti, ci costrinse a ricerche lunghe pazienti
(Marzotto N. 1925 Uve da vino: Descrizione e notizie ampelografiche, viticole ed enologiche dei vitigni più pregiati dell'alta e Media Italia. Vicenza, Italy: Tip. Commerciale)
Evidence of loss in cultivated grapevines diversity: The example of the Balearic Islands (Spain)
Sonia García Muñoz, Thierry Lacombe, Antoni Martorell Nicolau,
Félix Cabello
Submitted to Biodiversity and Conservation
Chapter 2
- 11 -
Abstract
The loss of grapevine varieties over the world has produced an important genetic erosion of the
gene pool. This problem is more notable in isolated areas for its particular singularity,
characterized by unique specimens, as the case of the Balearic Islands (Spain).
The aim of this study was to quantify the loss of grapevine from XVII century to nowadays; for
this purpose it was necessary a thorough investigation to identify the grapevine names found in
the antique bibliography. The ampelographic descriptions found in the consulted literature as well
as the available information in two grapevine repositories were used to compare the antique
descriptions with conserved grapevines nowadays. In this study, possible causes of the change in
the Balearic viticulture as several diseases (powdery mildew, flygreen or phylloxera plague), the
influence of the Quality Demarcation over evolution of the number of cultivars and vineyard area,
the origin of some cultivars as well as the entry of foreign varieties has been discussed.
More than 75% of the cultivars found in the bibliography could be identified. One of the most
interesting results was the high grapevine diversity found despite of the small geographic area,
being the islands an important stronghold for several cultivars. The greatest change over the
Balearic viticulture was dated before the phylloxera plague arrived to the islands contrary to what
was thought. The islands were divided in several viticulture areas each one with a reference
cultivar. The used methodology in this study was useful to quantify the loss of grapevine
variability. Unfortunately, nowadays some of the antique cultivars are lost or under risk of
extinction, since an important loss around 50% of grapevine diversity has been quantified through
time.
Key words: Conservation genetics, crop evolution, minor varieties, Vitis vinifera
Introduction
Based on DNA profiling results, there are around 5,000 grapevine varieties (Vitis vinifera L.)
around the world (This et al. 2006). This number is decreasing year by year in their natural
environmental conditions; evidences of grapevine loosing are obvious in several regions in the
world (Ulanovsky et al. 2002; Bessis 2007; Carimi et al. 2010). However some of these grapevine
varieties lost in their natural areas are conserved in germplasm repositories. Among the main
causes of grapevine variability lost highlights diseases as powdery mildew, phylloxera and the
homogenization of international wine market (This et al. 2006; Santiago et al. 2008). A few
varieties, such as Chardonnay, Sauvignon blanc or Cabernet-Sauvignon are increasing the
vineyard area surface worldwide and replacing the local cultivars (de Mattia et al. 2007; Cipriani et
al. 2010), these varieties are also present in most of the European Countries. The introduction of
this foreign material induces important changes in the local viticulture, especially in isolated areas
which are most sensitive to changes. This is the case of the Balearic Islands where the
Evidence of loss in cultivated grapevines diversity
- 12 -
preservation of local grapevines is critical. As a consequence of water barriers, the vegetal
material exchange in the Balearic Islands is only possible by sea, mostly with countries sited
around the Mediterranean basin. This isolation has promoted high levels of endemic and genetic
divergence (Filippetti et al. 2005; Zerolo and Cabello 2006), producing several unique genotypes
(García-Muñoz et al. 2011), in fact these island populations must be clear candidates for
conservation (Wilson et al. 2009).
Nowadays, the wine consumers are looking for a new sensorial experience tired from taste of
international varieties. Thus original wine varieties are starting to be on demand. At the same time,
Quality Demarcations are required original and high quality wine varieties which should be linked
historically to a geographic area (Meredith et al. 1999; Santiago et al. 2008). Therefore, the
interest in understanding the origin and genetic diversity of rescued germplasm from different
geographical areas is growing (Cipriani et al. 2010; Schneider et al. 2010). Unfortunately, the lack
of morphological descriptions (ampelography), agronomical characteristics and grapevine trade
records of antique varieties implies that sometimes is not possible to identify the cultivars collected
in the prospection (Sladonja et al. 2007; Laiadi et al. 2009; Santana et al. 2010).
The first steps toward the identification of varieties linked to an area must be to know the
evolution of the viticulture in that area, using as reference the number of cultivar and growing area
found in antique bibliography. This information is crucial to identify correctly the varieties found in
the new prospection. The principal aim of this work was to identify clearly the cultivars found in the
antique bibliography providing further information about the grapevines grown in the Balearic
Islands from XVII century to nowadays. Specifically we aimed to (i) to detect the synonyms and
homonyms, (ii) to understand the evolution of the number of cultivars and growing area, (iii) to
identify the varieties (Vitis vinifera L.) connected with the Balearic Islands, and finally (iv) to
quantify the varieties loss through time. This information is crucial to identify correctly the varieties
found in the new prospection and to analyze grapevine diversity lost. The knowledge of the
cultivars observed in the past may provide insights into how to preserve them in the future, giving
advices for cultivars conservation.
Material and Methods
Number of cultivars and vineyard area
The material used in this work comprised the information about grapevine found in the antique
bibliography; names of cultivars (Vitis vinifera L.) and vineyard area from XVII century to
nowadays; 438 books dealing with ampelography and viticulture were consulted for this purpose.
All the available information as grapevine names, synonyms, homonyms, growing and vineyard
surface area as well as ampelographic description found in the literature were collected in a
database in base of the cultivation year (if available) or publication year. Each accession name
collected in the bibliography was identified when it was possible to assign a “Prime name” (know
Chapter 2
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grapevine name). The “Prime name” was assigned based on the Spanish (BOE 2011), French
variety catalogue (IFV 2007) and the Vitis International Variety Catalogue (VIVC,
www.vivc.batz.de) considering: (1) the ampelographic descriptions, that were used to compare the
antique descriptions with the actual grapevines, (2) documents conserved about the studied
varieties (i.e. herbarium, drawings, photographs, known homonyms and synonym) in two of the
most important grapevine repositories in the world: Vitis Germplasm Bank (VGB) “Finca El Encín”
(IMIDRA, Alcalá de Henares, Spain) and INRA Grape Germplasm Repository of Domaine de
Vassal (Marsellan-plage, France, www1.montpellier.inra.fr/vassal), and (3) published varieties
names (i.e. Viticulture books, scientific papers). When a reference was not identified it was not
possible to assign a “Prime name", so these cultivars are referenced as “not identified”.
Data analysis
Redundant names per each year were eliminated of the data base to keep only one “Prime name”
per variety and year. The relationship between the number of cultivars (identified, not identified
and total) and the vineyard area was analyzed using linear regressions with the software XLSTAT,
2009 version.
Results and discussion
Names, synonyms and homonyms
The numbers of grapevine accessions collected in the literature and located in the Balearic Islands
were 756. The identified references were 575 (76% of the total) corresponding with 70 different
cultivars, since several cases of synonyms were detected (Table 1); in contrast 181 references
could not be identified (24%; Table 2). Based on this bibliographical survey, a great grapevine
biodiversity was found in the Balearic Islands despite of their small geographic area and isolation.
The important number of synonyms found in the bibliography could be due to names adaptations
or changes produced trough time (Reisch et al. 1993; Aradhya et al. 2003).
Homonyms were detected for the cultivars refereed in the bibliography as Batista, Vinaté and
Giró (Tables 1-2). Batista accessions matched with two different cultivars with hairy leaf (syn.
Canari and Luverdon) and with hairless one (not identified variety); Vinaté accessions matched
with the red cultivar Bobal and the white one Viñaté; Giró accessions matched with three different
cultivars: Mansés de Capdell (syn. Giró and Mancens), Garnacha (syn. Grenache) and Giro sardo
(sym. Giró ros). The problem with Giró identification has been pointed out previously (Hidalgo and
Candela 1972); however, these homonyms between Giró and Garnacha only exist in bibliography
since it is not found in natural conditions or in grapevine repositories nowadays.
Evidence of loss in cultivated grapevines diversity
- 14 -
Table 1. Prime name, berry colour, and accession names of the Identified varieties in the Balearic Islands. Varieties with * corresponding with the varieties registered in the Spanish or French varieties catalogue (BOE 2011; IFV 2007); † Most antique reference. In bold prime cultivars present in natural conditions in the Balearic Islands nowadays. N: Number of references where the varieties are cited
Prime name Berry colour
Accession names † Most antique
reference N
Aleluya blanca White Al.leluia blanca, Al.leuier, Aleluya
†, Al-
leluyas
In 1874, cited in Carretero et al. (1875)
4
Aleluya negra Black Al.leluia negra, Al.leluia negra, Aleluies, Aleluya
†, Aleluyas
Carretero 1875 9
Alfonso Lavallée / Alphonse Lavallée*
Black Alfonso lavalle INDO 1982 1
Aramon* Black Planta, Planta rica†
In 1874, cited in Carretero et al. (1875)
4
Argamusa White Agamusa, Agremuses
†, Argamusa,
Argamusas, Argamuses, Argamusses, Argemusa
Salvador de Austria 1869
11
Batista Black Batista, Batistes†
Salvador de Austria 1869
20
Beba / Valenci blanco* White Calop
†, Calop blanco, Calops, Grumer,
Jaumes, Mateu, Palop, Pansal, Pansal blanch, Sant Jaume
In 1730, cited in Martí (1978)
33
Beba roja Rose Calop rojo, Calops, Grumè, Polop áspero
†
Varcárcel 1765 6
Bobal* Black Boal, Bobal†, Vinate Carretero 1875 9
Cabernet-Sauvignon* Black Cabernet sauvignon BOE 1995 1
Callet* Black Callet Satorras 1892 18
Cañorroyo White Cañarroyo INDO 1982 1
Carcajolo / Monvedro* Black Beni Salem, Beni-salem, Beni-Salem, Beni-salem de Majorque
†, Binissalem
Odart 1845 7
Cardinal* Red Cardinal INDO 1982 2
Cayetana Blanca / Pardina* White Jaen Estelrich 1903 1
Chardonnay* White Chardonnay BOE 1998 1
Chasselas cioutat* White Peu de rata García-Muñoz et al. 2011
1
Cinsaut* Black Cinsault, Cinsaut†, Sinsó Satorras 1892 6
Corazón de cabrito / Cornichon blanc*
White Maimons blancs, Memeles de vaca
†,
Teta de Vaca blanca Salvador de Austria 1869
1
Eperó de Gall Black Eperó de gall, Esperons de gall†
Salvador de Austria 1869
3
Excursach / Murescu Black Cursac, Escumacs, Escursac, Escursach
†, Excursach,
In 1842, cited in Ferrer (1999)
13
Fernandella Black Fernandella†, Ferrandella, Ferrandilla Aragó 1871 8
Fogoneu* Black Fogonen, Fogonén, Fogoneu, Fogonéu, Fogoneu francés, Fogoneus
†
Salvador de Austria 1869
30
Gafarró Black Gafarró Anonymous 1978 1
Garnacha Tinta / Grenache* Black Garnacha
†, Garnacha negra, Garnatxe,
Giró, Girons, Granacha, Granatxa Vargas Ponce 1787 23
Garnacha Tintorera / Alicante Henri Bouschet*
Black Alicante, Híbridos Bouchet, Tintorer†
In 1874, cited in Carretero et al. (1875)
8
Giró / Mancens Black Chances de Capdell, Giró, Mancés
†,
Manset, Mansés de Capdell
In 1874, cited in Carretero et al. (1875)
11
Giró Ros / Giro sardo* Rose Giró, Girons†, Giró ros
Salvador de Austria 1869
4
Gorgollassa* Black Gargallosa, Gargollasa, Gargollassa, Gargollosa, Gorgollasa, Gorgollassa, Gorgollasses
†, Gorgollosa, Gorgolloso
In 1856, cited in Anonymous (1878a)
30
Hebén / Gibi White Panses García de los Salmones 1914
1
Chapter 2
- 15 -
Prime name Berry colour
Accession names † Most antique
reference N
Macabeo / Macabeu* White Macabeo Forteza 1944 2
Malvasia aromatica / Malvasia de Sardegna*
White Malvasia, Malvasía
†, Malvasía de
Banyalbufar In 1617, cited in Agustí (1722)
24
Mandón / Garró* Black Galmeta, Gamete, Garró, Mandó García de los Salmones 1893
5
Mansés de Tibbus Black Mancés d´en Tibus, Mancés d´en tibús, Mances den tibús
†, Mansés de Tibbus,
Mansesos de en Tibbus Carretero 1875 6
Manto Negro* Black Cabelis, Manto negro† Satorras 1892 12
Mazuela / Carignan* Black Carignan†, Cariñena Anonymous 1889 6
Merlot* Black Merlot BOE 2001 1
Merseguera* White Cañavella García de los Salmones 1914
1
Mollar Cano / Negra mol* Black Mollar Estelrich 1903 1
Monastrell / Mourvèdre* Black Mandó, Monastrell
†, Monestrell,
Morrastrell Salvador de Austria 1869
9
Moscatel de Alejandría / Muscat d´Alexandrie*
White
Momona de Pollenza, Moscatel†,
Moscatel de Málaga, Moscatel de Mallorca, Moscatel romano, Moscateles, Moscatell, Moscatell romá
In 1617, cited in Agustí (1722)
27
Moscatel de Grano Gordo Rosado / Muscat à petit grains roses*
Rose Moscatel
†, Moscatel rosado, Moscatel
vermeill Salvador de Austria 1869
6
Moscatel de Grano Menudo / Muscat à petit grains blancs*
White Moscatel menudo, Moscatel menudo blanco, Moscatel menut
†
Carretero 1875 6
Ohanes* White Ohanes Estelrich 1903 1
Palomino Fino / Listán Blanco* White Jerez, Listan, Ojo de liebre, Ull de llebre
†, Ulls de llebra
Salvador de Austria 1869
7
Pampolat girat Black Botget de parmpoml lluent, Cruxent, Cruixen, Pampal gira
†, Pampol girat,
Pampolat Odart 1845 10
Pardillo* White Pardillo INDO 1982 1
Parellada* White
Montana, Montaña, Montenach, Montona
†, Montonas, Montonec,
Montonés, Multonac, Multonachs, Muntona, Muntone, Parellada
In 1816, cited in Albertí and Rosselló (2007)
24
Pedro Ximénez* White Pedro Giménez, Pedro Jiménes, Pedro Jiménez
†, Pedro Ximénez, Pedro-
Ximinez Satorras 1878 8
Pensal Blanca / Moll* White Moll, Pansal blanco, Pensal blanc, Pensal blanco
†, Penzal blanco, Premsal
blanc Estelrich 1903 11
Pepita de oro White Pepita de oro INDO 1982 2
Picapoll Blanco / Piquepoul blanc*
White Picapoll blanco, Picapolla albilla† Despuig 1784 2
Pinot noir* Black Pinor noir BOIB 2005 1
Planta Fina de Pedralba / Farana*
White
Alicante blanco, Farana, Farrana, Ferrana, Ferrana blanca, Majorcain
†,
Majorquen, Majorquen blanc, Majorquin, Mallorqui, Mallorquí, Mallorquin, Mayorcain, Mayorcain blanc, Mayorquen, Mayorquen blanc, Mayorquin, Planta, Plantes
Odart 1845 23
Quigat White Cagat, Cagats
†, Masacamps, Quigat,
Quijat Salvador de Austria 1869
15
Riesling* White Riesling BOIB 2005 1
Roseti / Dattier de Beyrouth* White Roseti INDO 1982 1
Sabaté Black Sabaté, Sabater, Sabaters†
Salvador de Austria 1869
20
Santa Magdalena White Juanillo
†, Magdalena, Santa Magdalena,
Temprana Carretero 1875 9
Sumoll Tinto* Black Sumoll García de los Salmones 1914
5
Evidence of loss in cultivated grapevines diversity
- 16 -
Prime name Berry colour
Accession names † Most antique
reference N
Syrah* Black Syrah BOE 2003 1
Taferielt / Farana noir Black Ferrana negra García de los Salmones 1914
1
Tempranillo* Black Tempranillo, Tinto ribera, Ull de llebre†
García de los Salmones 1914
3
Teta de Vaca Tinta / Ahmeur Bou Ahmeur
Red Maimó, Maimons negres†
Salvador de Austria 1869
2
Trepat* Black Parrel Anonymous 1907 1
Valenci Tinto / Valensi noir* Black Balancí negre, Beba negra, Calop negro, Calop tinto, Grumés rojos
Salvador de Austria 1869
4
Valent Blanc White Valent blanch, Valent blanco, Valent-blanc, Valents blancs
†
Salvador de Austria 1869
15
Valent Negre Black Valent negre, Valent negro, Valent-negre, Valents negres
†
Salvador de Austria 1869
11
Viñaté White Panse valenciano, Vinaté, Vinater, Vinaters
†, Vinatés, Viñater
Salvador de Austria 1869
15
Xarello* White Cartuxa
†, Pansa valenciana
†, Xarel.lo,
Xarel-lo, Xarelló†, Xarello blanco, Xarelo
García de los Salmones 1914
7
Some of not identified varieties were referenced to a color as Blanquet, Bermell and Negrillo
(which means white, rose and black respectively), or to a generic names as Inglés (from England),
López (a very common Spanish surname) or Pansal (which means “to do raisin”; Table 2). In
some cases not identified varieties showed different grape colour which did not match with
nowadays conserved grapevines (e.g. Argamusa, Esperó de gall, Moll, Quigat; Tables 1-2). The
presence of not identified cultivars could be caused by identity lost or new crosses produced over
the years. Our previous results validate this second explanation since some unknown varieties
found in the last prospection did not match with other varieties around the world, therefore these
varieties could be considered unique genotypes (García-Muñoz et al. 2011). In any case, the
information lost about not identified references makes impossible their identification until now.
Table 2. Prime name, berry colour, and accession names of the not identified varieties in the Balearic
Islands
Accession names Berry colour Reference
Aigomel White Satorras 1878
Alaró Not specified Anonymous 1886
Alicantí Not specified Forteza 1944
Argamusa Black Hidalgo 1991
Asturell Black García de los Salmones 1914
Asturell White García de los Salmones 1914
Babarrés Not specified Anonymous 1889
Barbaresus White García de los Salmones 1914
Barroves White Carretero 1875
Batista Black Anonymous 1889
Batzoles Black Salvador de Austria 1869
Beberrés White García de los Salmones 1914
Bermell Not specified Rodríguez Navas 1904
Bermell Not specified Sánchez 1915
Blanquet White García de los Salmones 1914
Chapter 2
- 17 -
Accession names Berry colour Reference
Bragat Black Carretero 1875
Bregats Not specified Carretero et al. 1875
Calop moscatell Not specified Griera 1935
Calop-Giró Not specified Sánchez 1915
Calop-Giró Not specified Anonymous 1978
Calop-Pauzal Not specified Sánchez 1915
Cariavella White Carretero 1875
Castellanas Not specified In 1617, cited in Agustí (1722)
Champany Not specified Forteza 1944
Ciusant Not specified Ballester 1910
Ciusant Not specified Ballester 1911
Cruixó Not specified Griera 1935
Duricie White Carretero 1875
Esperó de gall White Carretero 1875
Estorell Black García de los Salmones 1914
Foguan Not specified Anonymous 1978
Font negro Black García de los Salmones 1914
Formigons Black Salvador de Austria 1869
Furmigó Not specified Carretero et al. 1875
Furmigó Black Carretero 1875
Giró Rose In 1616, cited in Alcover and Moll (2001)
Giro Not specified Despuig 1784
Giró Not specified In 1816, cited in Albertí and Roselló (2007)
Giró Not specified Weyler y Laviña 1854
Giró Not specified Anonymous 1861
Giró Not specified Anonymous 1878b
Giró Not specified Salvador de Austria 1869
Giró Not specified In 1889, cited in Albertí and Roselló (2007)
Giró Not specified Ballester 1911
Giró Not specified García de los Salmones 1915
Giró Not specified Forteza 1944
Giró Not specified Marcilla 1949
Giró Black or rose Griera 1965
Giro pansal Not specified Estelrich 1877
Gorch White García de los Salmones 1914
Gorgs Black Salvador de Austria 1869
Gorru Not specified Griera 1935
Gots Black García de los Salmones 1914
Graperas Not specified Forteza 1944
Grapesa Black García de los Salmones 1914
Grech Black García de los Salmones 1914
Grech White García de los Salmones 1914
Imperial White García de los Salmones 1914
Inglés Not specified Satorras 1892
Jaumillos Not specified Forteza 1944
Evidence of loss in cultivated grapevines diversity
- 18 -
Accession names Berry colour Reference
Juanillo White García de los Salmones 1914
Juanillo Not specified Marcilla 1949
Juanillo Not specified Anonymous 1950
Juanillo Not specified Hidalgo and Candela 1972
Juanillos White Salvador de Austria 1869
Leuries Not specified Salvador de Austria 1869
Llora Not specified Anonymous 1889
Lloras Black García de los Salmones 1914
Lloreta Black García de los Salmones 1914
Llorete Not specified Forteza 1944
Llurguei White Carretero 1875
Lopez Black Carretero 1875
Lopez Rose Satorras 1878
Lopez Black Anonymous 1889
Lopez Rose Satorras 1892
López Black Ballester 1910
López Not specified Ballester 1911
López Black García de los Salmones 1914
Loretes Black Salvador de Austria 1869
Maiblanch Black Carretero 1875
Mandó Not specified Forteza 1944
Mandó Not specified Anonymous 1950
Mandó Not specified Hidalgo and Candela 1972
Manzanilla Black García de los Salmones 1914
Mapisco Black Carretero 1875
Marol Black Carretero 1875
Mateu Black García de los Salmones 1914
Moll Black García de los Salmones 1914
Mollá White Carretero 1875
Mollá Not specified In 1885, cited in Pastor Sureda (1980)
Mollá Not specified Satorras 1892
Mollar Blanco Satorras 1878
Mollar Not specified Despuig 1784
Mollar Not specified Anonymous 1861
Mollar Not specified Salvador de Austria 1869
Mollar Not specified Anonymous 1878b
Mollar Not specified Anonymous 1889
Mollar vert Not specified Salvador de Austria 1889
Molls Black Salvador de Austria 1869
Moltona Black García de los Salmones 1914
Montona Not specified Weyler y Laviña 1854
Montona Black Salvador de Austria 1869
Montona White Satorras 1878
Montona White Ballester 1911
Mora Black García de los Salmones 1914
Chapter 2
- 19 -
Accession names Berry colour Reference
Morastel Black García de los Salmones 1914
Morete Not specified In 1842, cited in Ferrer (1999)
Moscatell Not specified In 1382, cited in Canals i Frontera (1989)
Muntona Not specified Despuig 1784
Mustrell Black García de los Salmones 1914
Negrelles Black Salvador de Austria 1869
Negrillo Not specified Satorras 1892
Ojo de liebre Not specified Ballester 1911
Padre White Carretero 1875
Padré White García de los Salmones 1914
Pampal Not specified Viala and Vermorel 1903
Pampals Not specified Viala and Vermorel 1903
Pampals Not specified Viala and Vermorel 1909
Pampégat Not specified Viala and Vermorel 1909
Pampol roda Not specified Salvador de Austria 1869
Pampol rodal Not specified Casas de Mendoza 1857
Pampol rodal Rose Rodríguez Navas 1904
Pampol rodat Not specified Vargas Ponce 1787
Pampol rodat Not specified Varcárcel 1791
Pampol rodat Not specified Weyler y Laviña 1854
Pampol rodat Not specified Anonymous 1878a
Pampol rodat Not specified Abela y Sáinz de Andino 1885
Pampol rodat White Anonymous 1889
Pampol rodat White Satorras 1892
Pampol rodat White Ballester 1910
Pámpol rodat White Matons 1928
Pàmpol rodat White Salvador de Austria 1869
Pampol rosat Not specified Despuig 1784
Pampol rosat Not specified In 1816, cited in Albertí and Roselló (2007)
Pampol rosat Not specified Anonymous 1861
Pampol rosat Not specified Salvador de Austria 1869
Pampol rosat White Carretero 1875
Pampol rosat Not specified Estelrich 1877
Pampol rosat Not specified Salvador de Austria 1889
Pampolrodat White Satorras 1878
Pampol-rosat Not specified In 1885, cited in Pastor Sureda (1980)
Pansa White Griera 1965
Pansal Black García de los Salmones 1914
Pansal Not specified García de los Salmones 1915
Pansal blanch White Satorras 1878
Pansal blanch White Anonymous 1889
Pansal blanch White Satorras 1892
Pansal blanch White Ballester 1910
Pansal blanch White Carretero et al. 1875
Pansal negre Black Carretero et al. 1874
Evidence of loss in cultivated grapevines diversity
- 20 -
Accession names Berry colour Reference
Pansal negre Black Satorras 1878
Pansal negre Black Anonymous 1889
Pansal negre Black Satorras 1892
Pansal negre Black Hernández Robredo 1903
Pansal negre Black Ballester 1910
Pansal negre Black Ballester 1911
Pansal negro Black Anonymous 1878b
Pansals Black Salvador de Austria 1869
Pansé Not specified Forteza, 1944
Panses Black Salvador de Austria, 1869
Panzal White Pacottet 1928
Parra borda Not specified Barceló y Combis 1879
Pauzal Not specified Sánchez 1915
Pensal blanch White Carretero 1875
Pensal negre Black Carretero 1875
Picapol Not specified Satorras 1892
Planta Not specified Estelrich 1877
Ponzal Not specified Anonymous 1978
Quigat Black Ballester 1910
Quigat Black Ballester 1911
Rosada Black García de los Salmones 1914
Sabaté White Homar Solivellas 1978
Tarrés Black Salvador de Austria 1869
Ullada Not specified Satorras 1892
Ullades Black Salvador de Austria 1869
Ulls de Llebra Not specified Carretero et al. 1875
Vennaçza Black Carretero 1875
Vernatxa Black García de los Salmones 1914
Vinaté Not specified Forteza 1944
Vinater Not specified Ballester 1911
Vinatés Not specified García de los Salmones 1915
Evolution and relationship between the number of cultivars and growing area
surfaces
The number of cultivars identified was always greater than the number of varieties not identified in
all the studied years (Figure 1). Nowadays, there are 36 identified cultivars in the Balearic Islands
(Figure 1) which matched mainly with the varieties allowed in several Demarcations of Quality.
The number of identified cultivars was not related with the vineyard area (r²=0.09; p=0.094),
however the not identified accessions as well as the total number of cultivars were significant
related with the vineyard area (r²=0.30; p=0.001; r²=0.18; p=0.013; respectively). The vineyard
area has decreased significantly through years (r²=0.63; p<0.001).
Chapter 2
- 21 -
0
10
20
30
40
50
60
70
1780 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000
Nu
mb
er
of
cu
ltiv
ars
Year
Identified Not Identified Total
Figure 1. Number of cultivars Indentified, Not Identified and Total per year found in the bibliography. Each
of the five periods is noted by roman numbers
Taking in consideration the tendency of the curve and the historical facts, the vineyard area
evolution could be grouped in 5 periods (Figure 2).
0
5000
10000
15000
20000
25000
30000
35000
1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000
Vin
eyard
are
a (
ha)
Year
Vineyard Area (ha)
Figure 2. Vineyard area (in hectare) per year found in the Bibliography. Each of the five periods is noted by roman numbers
In the first period, from the beginning of the study until 1870, the number of cultivars identified,
not identified and total was greater in 1869 (27, 18, 45 respectively; Figure 1). The vineyard area
surface decreased from 20,192 ha to 15,543 ha, related with the greenfly named “animaló”
(Haltica ampeloghaga Guer.; Salvator de Austria 1869) and the introduction in 1851 of powdery
mildew (Erysiphe necator Schwein.; Salvator de Austria 1869; Ballester 1911). These diseases
I II III IV V
I II III IV V
Evidence of loss in cultivated grapevines diversity
- 22 -
caused a drastic surface area reduction of several cultivars, especially in the case of most
appreciated as Escumacs (syn Excrusach), Garnacha (syn. Grenache), Giró, Malvasía, Mollar
(Salvator de Austria 1889) and Montona (Satorras 1892; Ballester 1911). As a result, during this
period there were attempts to recover the previous grapevine areas of these varieties, in example
Antonio de Cotoner tried to recover the vineyard area of Malvasia importing material from Malta
(Salvador de Austria 1869).
Among the second period (1871-1891), the vineyard area surface increased up to 22,833 ha
mainly caused by the appearance in 1863 of the phylloxera plague in France. Then Spain and
specifically the Balearic Islands improved the wine commercial relationship with France to export
their products (López y Camuñas 1878), that was promoted by a trade agreement between
France and the Balearic Islands in 1882 (Ripoll 1974). As a consequence, the local cultivars were
changed to satisfy the French demand (Ballester 1911), choosing cultivars more productive as
Callet and Manto Negro instead of less productive but more quality local cultivars as Gorgollasa,
which almost disappeared. In this second period the number of cultivars identified, not identified
and total was greater in 1875 (25, 20, 45 respectively; Figure 1). In this year, not identified
varieties achieved one maximum (20 cultivars), this might be caused by the high number of
imported cultivars. In fact, Antonio Cotoner and other vine growers had imported vegetal material
from France, Malta and South of Spain (Salvator de Austria 1869). As a consequence, the first
references to French varieties appeared, as Cinsaut (Satorras 1892) or Aramon which was
imported under the Spanish name of Planta Rica (meaning delicious plant) by Paulino Verniere
(Carretero et al. 1875; Estelrich 1877). Garnacha (syn. Grenache) was imported again from
Catalonia (Spain; Carretero et al. 1875), since the powdery mildew had caused a considerably
decrease over its growing area in the islands. During this second period some local cultivars as
Callet and Manto Negro were chosen for its high production of grapes and have been conserved
to nowadays. Based on these results, the most important events that changed the viticulture of the
Balearic Islands were dated in this period, before the phylloxera plague arrived to the islands.
Despite of the measures taken, the phylloxera plague was officially declared the June 4th of
1891 in the Balearic Islands (Pou y Bonet 1880). Consequently, in the third period (1892-1905) the
vineyard area surface decreased drastically to 3,075 ha until 1905. From 1906 to 1929 (fourth
period) the vineyard area increased to 8,826 ha, but never reached the previous cultivated area
before the phylloxera plague. The completely re-plantation of French vineyard (García de los
Salmones 1915) and the changes in land use, many growers decided to plant tomatoes (Albertí
and Rosselló 2007) and almonds instead of vineyard (Junta Consultiva Agronómica 1915), were
the causes of vineyard area contention. The long recovery period for vineyard could be due to the
bad results in the grafting (Martínez Rosich 1900) and the long time taken to find compatible
rootstocks with the calcareous soils (Pacottet 1928; Forteza 1944). In this period, in 1914 the total
Chapter 2
- 23 -
number of varieties increased up to 59, being the maximum of the identified, not identified and
total varieties (31, 28 and 59 respectively). The main cause of the maxima could be the research
of new material for the vineyard recovery, used to test the compatibility between several varieties
and rootstocks imported from the Iberian Peninsula. Also among this period, a rigorous studied
was done simultaneously in all the Spanish regions with a scientific objective to prevent the
grapevine diversity loss caused by the phylloxera plague, so numerous varieties were identified
and collected in natural conditions in Spain. These varieties were conserved in grapevine
repositories, therefore they have survived until nowadays.
During the fifth period, from 1930 to nowadays, the vineyard area has decreased to 1,795 ha.
The decreases of vineyard area from 1931 could be caused by three factors: first the Spanish Civil
War (1936-1939), second the touristic boom around 1960 and third the wine market expansion. In
contrast, there were slight increases of vineyard area from 1987 to 1990 and in 1997, around the
years when the two Denominations of Origin were created (Binissalem in 1991 and Pla i Llevant in
1999; Figure 1). The creation of the Denominations of Origins seems to influence positively on the
vineyard area. However, the appearance of Denominations of Origin reduced the number of local
cultivars planted. This is clearly shown in the lack of international varieties found in the
bibliography in 1982, whereas 25 years later, in 2008, six cultivars out of the 15 cultivars allowed
in the Balearic Islands Quality Demarcations are international varieties (Cabernet-Sauvignon,
Chardonnay, Merlot, Pinor noir, Riesling and Syrah). Therefore, the homogenization of the wine
marker is decreasing the local cultivars over the years, as it is reported in other viticulture areas
around the world (This et al. 2006; de Mattia et al. 2007; Carimi et al. 2010).
Cultivars connected with the Balearic Islands
The most antique grapevine references were the varieties “Moscatell” in 1382 (Canals i Frontera
1989), “Giró” in 1616 (Alcover and Moll 2001); “Castellanas” and Malvasía (prime name Malvasía
aromatic, synonym Malvasía de Sitges) in 1617 (Agustí 1722); Calop (prime name Beba, Valenci
Blanco) in 1730 (Martí 1978); “Mollar”, “Muntona”, “Pampol rosat” and Picapolla albilla (prime
name Picapoll Blanco) in 1784 (Despuig 1784); Garnacha (syn. Grenache) and “Pampol rodat” in
1787 (Vargas Ponce 1787; Table 1).
Among the identified cultivars, the most referenced cultivars were Beba (prime name Valenci
Blanco) with 33 references, followed with 30 references appeared the cultivars Fogoneu placed in
Porreras, Felanitx and Manacor (Estelrich 1877), and Gorgollasa located in the central part of the
Majorca Island (Estelrich 1877) and Inca (Anonymous 1878a). Moscatel (prime name Moscatel de
Alejandría) appeared in 27 references. Montona variety (prime name Parellada) was referenced
24 times and was located in Pollensa (Despuig 1784; Anonymous 1878a), and finally, Malvasía
variety (prime name Malvasía aromatic, synonym Malvasía de Sitges) shared the same number of
references which was placed in Esporles (Despuig 1784) and Banyalbufar (North of Majorica
Evidence of loss in cultivated grapevines diversity
- 24 -
Island; Anonymous 1878a; Ripoll 1974; Table 1). In base of these results, we hypothesize that the
Balearic Islands were divided in several viticulture regions, each one with a principal cultivar such
as Malvasía in Banyalbufar (Noth of the Majorca Island), Montona in Pollensa (Northwest of
Majorca Island), Gorgollasa in central of Majorca Island and Fogoneu in South-west of the
Majorca Island. Among the not identified varieties, Giró, Pampol rodat and Pampol rosat were the
most referenced cultivar names (Table 2).
Although the origin of Montona variety (prime name Parellada) is unknown, the Croatian
(Montona, Istria actual Croatia) or Italian origin has been suggested (Oliver Moragues 2000),
however other theory relates its name with the adaptation to the foot of the mountains
environmental conditions. The origin of Beba (prime name Valenci Blanco) and Excursach
(synonymy Murescu) seems to be related with the Moors during the Islam expansion (García-
Muñoz et al. 2011).
There are two cultivar names that seems to be specifically related with the Balearic Islands; the
black cultivar Binissalem (name of a village sited in the central of the Majorca Island) matched with
the variety Carcajolo (syn. Monvedro) and the white one Mayorquin (from the Majorica Island)
matched with Planta Fina de Pedralba (syn. Farana). These results were corroborated in VGB
“Finca El Encín” and INRA Domaine de Vassal repositories, based on the bibliographical
references, ampelographic descriptions and microsatellite data.
Binissalem (principal names Carcajolo, Monvedro) was located in Spain (the Balearic Islands,
Extremadura and Catalonia), Algeria, Corsica, Sardinia, Portugal (around Lisbon area, Algarve
and Alentejo) and Australia. Although its origin is unknown the North-African origin has been
suggested by Pulliat (1898). Our results suggest that this cultivar could come to Majorca Island in
VIII century when Majorca was brought by the Arab, and then kept to Portugal by the sea routes
during the XIV century. In fact, a sea route existed in 1301 which connected Majorca Island with
the Western Mediterranean and the Atlantic sea; there are references indicating that Majorcan
people trip to Lisbon (Portugal) in the year 1340 (Fulgosio 1870; Abulafia 1996).
The last cultivar that could be connected with the Balearic Islands is the white variety
Mayorquin (principal names Planta Fina de Pedralba and Farana), which was located in France
(Provence), Spain (León, the Balearic Islands and Cataluña) and Algeria. The origin of this cultivar
is unknown but it seems to have an Oriental origin since the Balearic Islands were a strategic point
in several sea routes from the East of the Mediterranean basin to the North of Africa (Abulafia
1996).
Varieties loss through time
The grapevine diversity has been drastically reduced in the Balearic Islands. Nowadays, only 36
varieties out of 70 identified varieties through time are found in natural conditions (51.4%), six of
Chapter 2
- 25 -
them (16.7%) are international varieties allowed in the Demarcations of Quality, whereas nine are
table varieties (25.0%). These results imply that we have almost lost a half of the identified
varieties in natural conditions. In addition, none of the not identified cultivars has been found in
natural conditions or grapevine repositories, perhaps we have lost all of them, including the
cultivar which showed a grape color variation with respect to the conserved cultivars. The used
methodology in this study was useful to quantify the loss of grapevine variability.
An interesting result was the case of the not identified cultivar related to “Pampolat”
designations which are not found in natural conditions. In VGB “Finca El Encín” an accession with
Pampolat girat name exists. This is the only cultivar that could be related with these references,
however Pampolat girat is a black variety and the references to the “Pampolat” designations are
white or rose.
The varieties connected with the Balearic Islands, Mayorquin (prime name Planta Fina de
Pedralba, Farana) and Benissalem (prime name Carcajolo, Monvedro), are not present in the
Balearic Islands however are found in natural condition in other countries (i.e. France, Algeria), so
their preservation seems to be guaranteed at the moment. However, Argamusa, Quigat and
Sabaté, which are unique genotypes not found out of the Balearic Islands (Spain), are reducing
their growing area year by year. Argamusa and Sabaté were considered with lower quality for
wines during XIX century, recommending their no cultivation (Carretero et al. 1875; Anonymous
1889), in fact this could be the reason because these cultivars are minor varieties nowadays.
Fortunately, the preservation of these minor varieties in grapevine repositories is saving the
dramatic loss of genetic variability.
Particularly interesting is the case of Gorgollasa variety which was an important cultivar cited in
central of the Majorca Island, although nowadays it is in risk of extinction. It was no longer grown
from the French phylloxera plague because of its low production, despite their wines obtained
several international prizes towards the end of the XIX century (Anonymous 1878b). Actually the
Quality Demarcation Binissalem (Balearic Islands, Spain) is trying to recover this antique cultivar.
Fortunately, several recent works (Albertí and Rosselló 2007) and new prospection are
rescuing some unique cultivars as Valent negre and Valent blanc (García-Muñoz et al. 2011),
which are preserved in several grapevine repositories for their preservation.
Conclusions
This is the first time that using all the information available we could be able to approach
accurately the number of cultivars lost and their evolution in relation with the changes over the
viticulture of a specific area. The most important events that changed the viticulture of the Balearic
Islands were dated before the phylloxera plague arrived to the islands. The creation of the
Demarcations of Quality has stabilized the number of cultivars, whereas the new interest on
Evidence of loss in cultivated grapevines diversity
- 26 -
antique cultivars to satisfy the new wine consumers demand has a positive effect over the antique
grapevines conservation.
The grapevine diversity found in the Balearic Islands was very high despite of their small
geographic area with 70 varieties identified. Most of the grapevine names found in the antique
bibliography have been identified and fortunately some of them have been conserved until
nowadays as Callet, Manto Negro or Gorgollassa. However, several grapevine names found in the
bibliography did not match with varieties cultivated nowadays and remains unknown.
The methodology used in this study might be of great interest to quantify the loss of grapevine
variability in other viticulture areas. The loss of grapevine diversity is evident; we have lost the half
of the identified cultivars growing in natural conditions in approximately 300 years. The high rate of
grapevine loss needs to provide roles of conservations. Special attention is recommended for the
unique genotypes not found around the world out of the Balearic Islands as Argamusa, Sabaté or
Quigat varieties which are decreasing their growing area. The rescue of the cultivars found in the
new prospection contributes to the conservation of the grapevine genetic variability. They must to
be preserved in grapevine resources before the loss of these cultivars is irreversible.
Acknowledgements
Financial support from INIA, project RTA 04 175-C3-3 and from ERDF resources. Sonia García
Muñoz was supported by a PhD scholarship from INIA “Caracterización y selección de variedades
de vid de Baleares”. Sonia García-Muñoz thanks to INRA Montpellier (France) and the Istituto di
Virologia Vegetale (Grugliasco, Torino, Italy) for welcoming her during her visiting period, as well
as for help her in the look up through the French and Italian bibliography references.
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Chapter 3
Hopeful
Ampelography: an old technique with future uses, the case of minor varieties of Vitis vinifera L. from the
Balearic Islands
Sonia García Muñoz, Gregorio Muñoz Organero,
María Teresa de Andrés, Félix Cabello
Submitted to Journal International des Sciences de la Vigne et du Vin (2010)
Chapter 3
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Abstract
The aim of the present work was to evaluate the ampelographic descriptions, genetical analysis,
agronomic characterization, must variables and phenology of 27 minor grapevine accessions from
the Balearic Islands (Spain). The influence of different agronomic variables, occasional climatic
phenomena (hailstorm) and the ampelographer’s experience were studied.
Grapevine accessions were analysed using 58 OIV qualitative and quantitative descriptors and
6 SSR. Ampelography is a good preliminary technique for the clarification of vegetable material,
confirming the microsatellite results. The colour of the young leaf’s upper side (OIV-051), the
juiciness of the flesh (OIV-232) and the firmness of the flesh (OIV-235) were the most difficult
characters to distinguish by ampelographers. In spite of the greater similarity found among
varieties studied, there are certain strong key characters for identification of these varieties (OIV-
225, OIV-084, OIV-053, OIV-004). In addition, the ampelographic descriptions, agronomic
parameters and phenology were influenced by occasional climatic phenomena (hailstorm).
The morphological and molecular characterizations of 27 accessions recollected in Balearic
Islands (Spain) were allowed to validate the ampelographic description method and grouped them
in 17 vine varieties. The genetic analysis showed that Beba blanca as a possible somatic mutant
from Beba roja. The hailstorm increased the vegetative period, specially affected at mature leaf,
bunch, agronomic characteristics and the composition of the must.
The present work characterize, for the first time, the ampelographic and molecular profiles of
these minor varieties. The study showed potential and interest of the cultivars suggesting that their
utilization may be important for the farmers.
Key words: Morphology, grapevine, agronomic characterization, descriptors, hailstorm influence
Introduction
Until a few years ago, ampelography has been the main method used for describing and then
identifying vine varieties (Schneider et al. 2008). Studies of ampelography using well defined OIV,
UPOV and UPGRI official descriptors supplied insights into clarification of vegetable material
(Pavek et al. 2003). The notations on the phenotypic characteristics of different cultivars taken by
important ampelographers over one hundred years ago (Odart 1874) have been confirmed years
afterwards by genetics (Schneider et al. 2008), validating ampelography as a preliminary method
for the clarification of vegetable material. However, in the last few years, confusing vegetable
material due to a lack of ampelographic knowledge has led to legal and commercial controversies.
In the last years molecular markers, and specially microsatellites, has became an essential tool
for the identification of grape varieties (This et al. 2004). They do not show the problem of
interaction with the environment or subjective interpretations, often attributed to some
Ampelography: an old technique with future uses
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ampelographic descriptors. And they make easier the exchange of results between laboratories by
comparison of genetic profiles. However differences based on somatic mutations such as the
colour of the berry, only can be certified if the material is evaluated in the field (Fatahi et al. 2003).
Then, for clonal verifications the microsatellite technique is not valid (Cabezas et al. 2003).
Therefore, it is also necessary to have the description of the varieties, given that the genetic
analyses showed no difference from the original plants (Bessis 2007). For these reasons,
molecular markers must be complemented by ampelography (Crespan et al. 2008; Cunha et al.
2009).
The number of cultivated vine varieties has dropped in most European countries, since most of
the new plantations are based on varieties included under quality designations (Designations of
Origin). As a result, many of the old indigenous varieties are at risk of becoming extincted (de
Mattia et al. 2007). However, nowadays wine consumers are demanding new products which
increased the interest of winemakers and researchers on traditional old minor varieties (Santiago
et al. 2008). In this way, the use of traditional ampelography has re-emerged as the only way to
verify whether the material found corresponds or not to the material quoted in the bibliography, by
comparing the ampelographic descriptions (Cervera et al. 2001).
The ampelographic and agronomic characterization of minor varieties must been studied as a
pre-requisite in order to include them in a germplasm collection (Alleweldt and Dettweiler 1989).
Simultaneously, ampelographic description is compulsory for inscribing the varieties in the register
of commercial varieties or in the variety catalogue (Chomé et al. 2003; UPOV 2007), before vine
growers may used them. At the same time, the ampelographic technique is cheaper and more
accessible than the microsatellite where a specialised laboratory is required. So, the
morphological description of the vine varieties could be used to prevent possible mistakes in the
plantations, nurseries or vine growers, and can be later verified by the microsatellite techique. The
combination of molecular markers and ampelographic descriptions irrefutably certifies the varietal
identification in a more thorough way and leads to more reliable and objective results (Hinrichsen
et al. 2001; Santiago et al. 2005).
Some authors suggest that atmospheric changes have an influence on morphological
characteristics (Alleweldt and Dettweiler 1989; Cunha et al. 2009) and vineyard management
creates changes in ampelographic descriptions (Bowers et al. 1993). Because of that,
morphological descriptions have been discredited and considered less reliable because it can be
confused with atmospheric changes and they may be the result of subjective evaluations (Cervera
et al. 2001). Knowledge of the factors that influence morphological descriptions, therefore, is an
important requirement for developing a well defined ampelographyc characterization. In this work
we have analyzed 27 accessions from Balearic Islands (Spain) vine varieties (Vitis vinifera L.),
where previous morphological studies have not been performed. Therefore the ampelographic
Chapter 3
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descriptions are the first step towards to the identification and the full understanding of the
agronomic and genetic structure of these minor varieties. These varieties were collected from
1914 to 2005 in all the geography of Balearic Islands and then introduced in the Vitis Germplasm
Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain), however, nowadays some of this
varieties are not in their original location. An accurate identification of these varieties is crucial to
preserve the genetic variability, as well as to encourage their use and to provide new wine
products to consumers.
Therefore we examine the following objectives: (1) whether 27 vine accessions differ among
themselves using morphological and genetical analysis methods, (2) whether the identification of
the most influential descriptors and their relationships with the described varieties could help to
make easier the field descriptions, (3) whether occasional climatic phenomena (hailstorm)
conditioned agronomic, ampelographic descriptions and phenology and (4) whether the
ampelographer’s experience and objectivity influences ampelographic descriptions, identifying the
more difficult descriptors.
Material and Methods
Site description and climate data
The study was carried out at the Vitis Germplasm Bank (VGB) “Finca El Encín” (IMIDRA, Alcalá
de Henares, Spain; 40º31´N, 3º17´W, 610 m asl, semiarid Mediterranean climate). The climatic
data were obtained from the meteorological station located inside the VGB. Average precipitation
(mm) and temperature (ºC) were summarized monthly over the study period (January 2006-
December 2007), since certain climatic parameters may interfere in ampelographic descriptions
(Alleweldt and Dettweiler 1989). The 52-years mean monthly rainfall and temperature for this
station were also included (period 1956-2007).
Plant material
The material described coincides with the varieties of Vitis vinifera L. introduced in the Vitis
Germplasm Bank (VGB) “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain) from Balearic
Islands (Table 1), except three accessions of Beba variety (O16, O17, O44), coming from
Valencia and the variety Pampolat girat coming from Tarragona. Twenty seven different
accessions planted in 2002 were described, all of which are grafted on Richter 110, simple cordon
pruning, eight buds per vine and were adult plants. The planting density was 4808 vines ha-1 (0.80
m 2.60 m).
Ampelography: an old technique with future uses
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Table 1. Plant material from the Balearic Islands located at the Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain). Local names=names of the accession; Acc.: accession; Id.: code used in the analysis; Berry Colour: N=red, B=white, RS=rose; Use: W=wine, T=table
Local names Acc. Id. Berry colour
Use Bank code
Batista E23 BAT.E23 N W ESP080-BGVCAM1109 Mateu E39 BEB.E39 B W ESP080-BGVCAM0901 Corazón de Ángel O16 BEB.O16 B W ESP080-BGVCAM0890 Valenci blanco O17 BEB.O17 B W ESP080-BGVCAM1097 Grumiere blanco O44 BEB.O44 B W ESP080-BGVCAM2187 Jaumes E14 BEB.E14 B W ESP080-BGVCAM1543 Calop E32 BEB.E32 B W ESP080-BGVCAM1119 Calop blanco E43 BEB.E43 B T ESP080-BGVCAM2325 Beba negra, Calop negro, Calop negre
E18 BEN.E18 N T, W ESP080-BGVCAM1537
Calop rojo, Calop roig E19 BER.E19 RS W ESP080-BGVCAM1538 Vinaté, Vinater E11 BOB.E11 N W ESP080-BGVCAM1550 Boal E21 BOB.E21 N W ESP080-BGVCAM1535 Callet E26 CAL.E26 N W ESP080-BGVCAM1118 Eperó de Gall, Esperó de gall E37 EPE.E37 N W ESP080-BGVCAM0893 Excursach, Escursach E27 EXC.E27 N W ESP080-BGVCAM1131 Fogoneu E24 FOG.E24 N W ESP080-BGVCAM1132 Mancés de Capdell E31 GIR.E31 N W ESP080-BGVCAM1143 Giró E36 GIR.E36 N W ESP080-BGVCAM1134 Gorgollasa, Gorgollassa E25 GOR.E25 N W ESP080-BGVCAM1135 Mansés de tibbus, Mancés de Tibbus
E30 MES.E30 N W ESP080-BGVCAM1144
Manto Negro E29 MTN.E29 N W ESP080-BGVCAM1145 Cabellis E20 MTN.E20 N W ESP080-BGVCAM1536 Pampolat Girat H24 PAM.H24 N W ESP080-BGVCAM1884 Pensal blanco, Moll, Prensal blanco SCL PEN.SCL B W ESP080-BGVCAM2682 Massacamps E38 QUI.E38 B W ESP080-BGVCAM0900 Quigat E33 QUI.E33 B W ESP080-BGVCAM1159
Sabaté, Sabater E35 SAB.E35 N W ESP080-BGVCAM1162
Ampelographic description
Ampelographic descriptions were carried out during two consecutive years (2006 and 2007;
UPOV 2007). Additionally old descriptions developed in 1998 and 1999 (stored in VGB) were used
as reference. The observations were made according to Office International de la Vigne et du Vin
(OIV 1984) specifications, recording a total of 58 characters in the precise phenological state
(including qualitative and quantitative characters; Table 2). During year 2006 descriptors
recommended by OIV (1984) modified by Genres 081 were used (www.genres.de/vitis/vitis.htm),
but in 2007 we used updates for the descriptors published, which are specified in the project
Grapegen 06 (http://news.reseau-concept.net). Therefore, it was necessary to standardize
descriptions of 1998-1999 and of 2006 according to 2007 descriptors. All varieties were described
with a minimum of five plants (UPOV 2007). A minimum of 10 young shoot, adult leaves, shoots,
bunches and woody shoot for each vine were sampled, except for berries which 30 belonging to
the middle of ten representative bunches were took. All varieties were described by three
Chapter 3
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ampelographers with different lack of experience, therefore, a minimum of 30 data for each of the
descriptors were provided. At least two repetitions for each of the descriptions in each of the
accessions were realized, selecting the modal value as a final description.
Table 2. List of the descriptors used (OIV 1984)
Parameters Part plant Descriptors
Qualitative parameters
Shoot tip OIV-001, OIV-002, OIV-003, OIV-004
Young leaf and shoot OIV-006, OIV-007, OIV-008, OIV-015-2, OIV-016, OIV-051, OIV-053
Shoot OIV-103
Flower OIV-151
Mature leaf
OIV-067, OIV-068, OIV-070, OIV-072, OIV-074, OIV-075, OIV-076, OIV-079, OIV-080, OIV-081-1, OIV-081-2, OIV-082, OIV-083-1, OIV-083-2, OIV-084, OIV-087, OIV-094, OIV-306
Bunch OIV-204, OIV-208, OIV-209
Berry OIV-220, OIV-221, OIV-223, OIV-225, OIV-230, OIV-231, OIV-232, OIV-235, OIV-236, OIV-241
Fenologhy OIV-301, OIV-303
Quantitative parameters
Yield variables OIV-155, OIV-351, OIV-504
Bunch, berry OIV-202, OIV-203, OIV-206, OIV-502, OIV-503
Must OIV-233, OIV-505, OIV-506, OIV-508
Molecular characterisation: microsatellite analysis
DNA was isolated by extraction from young leaves using the DNeasy Plant kit (QIAGEN,
California, USA). A set of 6 microsatellite loci proposed by the GENRES 081 Project (European
Vitis Database, www.genres.de/vitis/vitis. htm) was analyzed in order to verify the varietal identity
as described Martín et al. (2003) with minor modifications such as substitution of microsatellite
ssrVrZAG47 by VVMD27. PCR amplifications were analyzed by an ABI 3130 Genetic Analyzer
(Applied Biosystem, Foster City, CA, USA), and the fragments were sized with GeneMapper 4.0
software using GeneScan-LIZ 500 as internal marker (Applied Biosystems). To compare our
results with another dataset different varieties were used as reference (Cabernet-Sauvignon,
Chardonnay, Granache, Merlot and Mourvèdre; This et al. 2004).
Agronomic and must variables
The following agronomic variables were recorded: yield variables (number of bunches per shoot,
number of bunches per vine (on each of the vines in harvest), number of woody shoots per vine,
yield (kilograms grape per vine) and woody shoot weight (on each of the vines in pruning));
variables of bunch and berries like bunch length (stalk excluded), bunch width, stalk length, total
bunch length, single bunch weight, berry weight and number of berries per bunch (recorded for 10
representative bunches).
Ampelography: an old technique with future uses
- 38 -
The recorded must variables were: must yield (%) (OIV-233), following the methodology
described by Santiago et al. (2008): the supernatant obtained for this measurement was used to
determinate the probable alcohol content (%VOL; OIV-505) that was first measured in ºBrix, using
a hand held Brix refractomer (PR-101, Palette, ATAGO); then a conversion Table was used to
determine the probable alcohol content (%VOL); pH (OIV-508) was measured using a pH meter
(Crison Micro GLP21, Alella, Barcelona, Spain) and total acidity (g L-1 Tartaric acid; OIV-506);
where berries from the central part of each representative bunch were selected, crushed and the
total acidity of the must calculated described by Santiago et al. (2008).
Phenology
Phenology annotation, following the methodology of Baggiolini (1952), was conducted three times
a week throughout the vineyard growing season, except during flowering and veraison in which
annotation has been daily.
Data analysis
The climate data were analyzed using descriptive statistics, whereas the agronomic and
ampelographic description dataset was analyzed using both, multivariate and univariate methods.
Multivariate methods were used to observe the similarity between groups of varieties based on
ampelographic data. Firstly, the ampelographic dataset was reduced by removing all unchanged
characters which that discriminating power. Hierarchical clusters were carried out, using
correlation coefficient (Martínez de Toda and Sancha 1997; Cervera et al. 2001) and the
"Unweighted Pair -Group Method Analysis” (UPGMA) as linkage method (Cervera et al. 2001;
García-Muñoz et al. 2005). As a characterization was developed during two consecutive years
different dendrograms were generated for each studied year.
Correspondence analysis (CA), based on the first ampelographic data matrix, was carried out
to identify the most influential descriptors and their relationships with the described varieties
(Alleweld and Dettweiler 1989; Agresti 2002; García-Muñoz et al. 2005). The correlations between
the descriptions of the years 2006 and 2007 as well as the 1998-1999 reference descriptions
available in VGB were analysed using two-way Mantel tests (Legendre and Legendre 1998). This
analysis has been used also to test if a spot phenomenon such as a hailstorm generated
differences on the descriptions.
Univariate methods comprised Student-t test for paired data to evaluate the existence of
significant differences between the quantitative descriptors measured in the years 2006 and 2007
over the same individuals (Crawley 2007). Quantitative descriptors (that are qualitative characters
according to OIV) were those of agronomic interest related to bunch, must as well as berry weight,
number of bunches per shoot, number of bunches per vine, yield (kg per vine), number of woody
shoots per vine and weight of woody shoots per vine. Integer data variables (number of berries
Chapter 3
- 39 -
per bunch, number of bunches per woody shoot and number of bunches per vine, number of vine
shoots per vine) were transformed using log-transformation (Crawley 2007).
To test the existence of discrepancies between the qualitative ampelographic descriptions
considering the experience of the observer, as well as the existence of different degrees of
difficulty in describing the OIV descriptors, generalized linear models (GLM) were fitted with log-
link function and poisson errors (Crawley 2007). In this analysis only 35 characters were used
excluding the characters related to length and width berry (OIV-220, OIV-221), shoot attitude
(OIV-006), phenology, quantitative characters and those characters that were stables in all
accessions (OIV-001, OIV-016, OIV-151, OIV-230, OIV-231 and OIV-241). For each of the 35
descriptors a contingency table was made relating the experience of observer and controls versus
the number of occurrences presented at each level of expression for each one of the OIV
descriptors. Over these tables two models were fitted for each year (2006 and 2007), following the
recommendations for measuring the correlation between observers proposed by Agresti (2002)
and considering control observation as reference. The modal data of individual replicates for each
descriptor was used as a control for the discrepancies, although if in doubt the ampelographic
photography descriptions results were used. Finally, binomials test for two proportions were used
to analyse the differences between the presence/absence of teeth on the petiole sinus, number of
wings per bunch and fenology for the years 2006 and 2007 (Crawley 2007). The proportions were
calculated for each expression level of each OIV descriptors for each year.
The statistical analysis of similarity, hierarchial clusters, CA and Mantel test were performed
with the software Numerical Taxonomy System (NTSYS v.2.1; Rohlf 2000). The remaining
analyses (generalized linear models, Student's t for paired data and analysis for comparing two
binomial proportions) were performed using R software environment (version 2.8; R Development
Core Team 2008).
Results
Climatic data
The temperature and precipitation observed during the period 2006 and 2007 was similar to 52-
years series average, although in year 2007 the rainfalls showed an atypical distribution (Figure
1). While, the rainiest period of 2006 was winter, in 2007 the period with the most rain was spring,
caused due to the hailstorm that took place on May 20th in which 43 mm were collected, which
represents 44% of that month’s rain. This hailstorm phenomenon in the area being studied occurs
with a frequency of once every 5 years.
Ampelography: an old technique with future uses
- 40 -
0
20
40
60
80
100
120
0
10
20
30
40
50
60
E F M A M JN JL A S O N D
2006 Mean temperature (12.2 ºC) 2007 Mean temperature (13.0 ºC)
52 Years mean temperature (13.4 ºC) 2006 Total rainfall (430 mm)
2007 Total rainfall (399 mm) 52 Years mean rainfall (430 mm)
Figure 1. Monthly mean precipitation (mm) and temperature (ºC) for the years 2006, 2007 and 52-years period, based on “Finca El Encín” Meteorological Station (40º31´N, 3º17´W, 610 m asl, Alcalá de Henares, Madrid, Spain)
Differences among varieties using morphological and genetic approaches
The description carried out in the year 2006 showed a high degree of similarity between the
repetitions of one single accession (Figure 2). The lowest rate of similarity was found in Batista
variety and showed a rate of 0.85 due to a visible adaptation problem on the plot. Excluding this
information, the similarity rate between repetitions of a single accession was very high, ranging
from 0.92 (Pampolat girat) to 0.99 (Manto Negro; E20 accession). At a similarity level of 0.75, four
groups can be distinguished (Figure 2). The first one, with a 0.75 similarity level among
individuals, was made up by the red Beba Negra and Fogoneu varieties. The second group, with a
high similarity level among groups of 0.90, is formed by three red varieties, Excursach, Callet and
Manto Negro, this varieties generally have very low-intensity colouration of the prostrate hairs of
the tip and no or very low presence of prostrate hairs between the main veins of both the young
leaf and the adult leaf. It must be stressed that the Callet and Manto Negro varieties had a 0.93
similarity coefficient. The following group, with a similarity between its individuals of 0.81, is
formed by a rose variety (Beba roja) and three white varieties, Pensal Blanca, Beba blanca (the
similarity between this variety’s seven accessions studied is 0.91) and Quigat. The last group, with
a similarity between individuals of 0.78, is formed by the red varieties Eperó de gall, Bobal,
Gorgollasa, Pampolat girat, Giró, Mansés de Tibbus and Sabaté, the similarity between the
repetitions for the same accession was always more than 0.90. In contrast with the other red
varieties, these have an average or high number of hairs on the tip and also between the adult leaf
and the young leaf’s main veins. The Batista variety is separated from all the varieties, although it
would be much closer to last two groups, but with lower similarity level (0.61).
Chapter 3
- 41 -
1.00 0.80 0.60 0.40
SAB.E35.RSAB.E35.3
SAB.E35.2SAB.E35.1
MES.E30.RMES.E30.4
MES.E30.3MES.E30.2
MES.E30.1
GIR.E36.R
GIR.E36.2GIR.E36.1
GIR.E31.RGIR.E31.2
GIR.E31.1
PAM.H24.R
PAM.H24.4PAM.H24.3
PAM.H24.2
PAM.H24.1
GOR.E25.RGOR.E25.4
GOR.E25.3GOR.E25.2
GOR.E25.1
BOB.E21.R
BOB.E11.REPE.E37.R
EPE.E37.4EPE.E37.3
EPE.E37.2
EPE.E37.1
QUI.E33.RQUI.E33.2QUI.E33.1
QUI.E38.R
QUI.E38.2QUI.E38.1
BEB.E43.RBEB.E32.R
BEB.E14.R
BEB.O44.R
BEB.O17.RBEB.O16.R
BEB.E39.RPEN.SCL.R
PEN.SCL.3PEN.SCL.2
PEN.SCL.1BER.E19.RBER.E19.3
BER.E19.2
BER.E19.1BAT.E23.R
BAT.E23.3BAT.E23.2
BAT.E23.1
MTN.E20.R
MTN.E20.2MTN.E20.1
MTN.E29.R
MTN.E29.2
MTN.E29.1CAL.E26.R
CAL.E26.4CAL.E26.3CAL.E26.2
CAL.E26.1EXC.E27.R
EXC.E27.4
EXC.E27.3EXC.E27.2EXC.E27.1
FOG.E24.R
FOG.E24.3FOG.E24.2
FOG.E24.1
BEN.E18.R
BEN.E18.4BEN.E18.3
BEN.E18.2BEN.E18.1
Similarity
Dendrogram 2006
Figure 2. Cluster dendrogram based on morphological relations among Balearic Islands vine varieties described in 2006 (Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain), using UPGMA method and correlation coefficient. Codes in Tab 1. The number after accessions indicates repetitions and R is the modal result of the descriptions realized, whereas lines indicate 0.75 and 0.90 similarity cuts
Ampelography: an old technique with future uses
- 42 -
Regarding the descriptions of 2007, the rate of similarity between the repetitions of one single
accession is very similar to the observed in 2006 (Figure 3). Again, Batista variety showed the
lowest similarity rate between repetitions (0.89). For the remaining accessions, the similarity rate
for a single accession varies from 0.92 for Pensal Blanca and Pampolat girat varieties to 0.98 for
Callet, Beba roja and Sabaté. The grouping of varieties in the year 2007 agree largely with the
grouping obtained with the 2006 data, although with certain differences that could be due to the
damage caused by the hailstorm (Figures 2, 3).
1.00 0.80 0.60 0,40
SAB.E35.R
SAB.E35.2
SAB.E35.1
GIR.E31.R
GIR.E36.R
MES.E30.R
MES.E30.2
MES.E30.1
EPE.E37.R
EPE.E37.2
EPE.E37.1PAM.H24.R
PAM.H24.1
PAM.H24.2GOR.E25.R
GOR.E25.2
GOR.E25.1
BOB.E21.R
BOB.E11.RQUI.E33.R
QUI.E38.R
PEN.SCL.R
PEN.SCL.2
PEN.SCL.1
BER.E19.R
BER.E19.2
BER.E19.1
BEB.E43.R
BEB.E32.R
BEB.E14.R
BEB.O17.R
BEB.O16.RBEB.O44.R
BEB.E39.R
BAT.E23.R
BAT.E23.2BAT.E23.1
MTN.E20.R
MTN.E29.R
CAL.E26.R
CAL.E26.2
CAL.E26.1
BEN.E18.R
BEN.E18.2
BEN.E18.1
EXC.E27.R
EXC.E27.2
EXC.E27.1
FOG.E24.R
FOG.E24.2
FOG.E24.1
Similarity
Dendrogram 2007
Figure 3. Cluster dendrogram based on morphological relations among Balearic Islands vine varieties described in 2007 (Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain), using UPGMA method and correlation coefficient. Codes in Tab 1. The number after accessions indicates repetitions and R is the modal result of the descriptions realized, whereas lines indicate 0.75 and 0.90 similarity cuts
Chapter 3
- 43 -
The Mantel test showed a good correlation among all the descriptions. The 2006 and 2007
descriptions showed a correlation rate of 0.87 and 0.74 with the 2006 and 1998 and 1999
(ampelographical reference descriptions). The best correlations between 2006 and 2007 for each
one of the described plant parts were obtained in shoot tip (r=0.92), and the worst correlation in
vine shoot (r=0.61), whereas mature leaf (r=0.75), berry (r=0.69) and bunch (r=0.65) showed
intermediate correlation coefficients.
Regarding the genetic characterization, the six SSR markers selected grouped the 27 samples
analyzed in 16 different genotypes. The white accessions E39, O16, O17, O44, E14, E32 and E43
showed the same microsatellite profile as Beba roja (E18). The two accessions E11 and E21
shared the same microsatellite profiles, similar results were showed by E31 and E36, E20-E29
and E33-E38, respectively (Table 3). These results are in agreed with the ampelographyc
descriptions.
Table 3. Genetic profiles at six microsatellite loci (in pb) of the 27 vine accession. Local names=names of the accession; Id.: code used in the analysis; Main name: name of the variety. Cabernet-Sauvignon, Chardonnay, Granache, Merlot and Mourvèdre varieties were used as reference (Ref)
Local names Id. Main name
VVMD7
VVS2
VVMD5
VVMD27
ZAG62
ZAG79
Batista BAT.E23 Batista
237 251
141 151
229 235
179 189
188 200
245 251
Mateu BEB.E39 Beba
241 247
133 141
233 237
181 189
188 204
243 247
Corazón de Ángel BEB.O16 Beba
241 247
133 141
233 237
181 189
188 204
243 247
Valenci blanco BEB.O17 Beba
241 247
133 141
233 237
181 189
188 204
243 247
Grumiere blanco BEB.O44 Beba
241 247
133 141
233 237
181 189
188 204
243 247
Jaumes BEB.E14 Beba
241 247
133 141
233 237
181 189
188 204
243 247
Calop BEB.E32 Beba
241 247
133 141
233 237
181 189
188 204
243 247
Calop blanco BEB.E43 Beba
241 247
133 141
233 237
181 189
188 204
243 247
Beba negra, Calop negro, Calop negre
BEN.E18 Valenci Tinto
237 241
135 141
225 233
181 189
188 196
243 247
Calop rojo, Calop roig BER.E19 Beba
241 247
133 141
233 237
181 189
188 204
243 247
Vinaté, Vinater BOB.E11 Bobal
237 241
144 146
225 231
181 189
188 188
243 247
Boal BOB.E21 Bobal
237 241
144 146
225 231
181 189
188 188
243 247
Callet CAL.E26 Callet
237 247
131 141
233 237
181 189
188 196
243 247
Eperó de Gall, Esperó de gall
EPE.E37 Eperó de gall
241 247
131 144
223 231
179 181
188 204
243 257
Excursach, Escursach
EXC.E27 Escursach
237 237
141 144
223 237
181 181
188 196
247 251
Fogoneu FOG.E24 Fogoneu
237 245
131 144
233 237
179 181
196 204
247 251
Mancés de Capdell GIR.E31 Giró
245 247
131 131
223 233
179 181
204 204
247 247
Giró GIR.E36 Giró
245 247
131 131
223 233
179 181
204 204
247 247
Gorgollasa, Gorgollassa
GOR.E25 Gorgollassa
237 247
141 151
219 237
179 194
188 204
257 261
Mansés de tibbus, Mancés de Tibbus
MES.E30 Manses de tibbus
241 247
131 151
219 223
181 181
188 204
243 243
Manto Negro MTN.E29 Manto Negro
237 241
131 144
231 233
181 194
186 188
247 257
Cabellis MTN.E20 Manto Negro
237 241
131 144
231 233
181 194
186 188
247 257
Pampolat Girat PAM.H24 Pampolat girat
241 247
131 141
233 237
181 183
188 204
247 259
Pensal blanco, Moll, Prensal blanco
PEN.SCL Pensal Blanca
237 241
135 141
231 231
179 194
188 196
249 257
Massacamps QUI.E38 Quigat
241 247
144 151
229 231
181 181
186 188
247 261
Quigat QUI.E33 Quigat
241 247
144 151
229 231
181 181
186 188
247 261
Sabaté, Sabater SAB.E35 Sabaté
237 241
131 131
225 231
185 194
188 196
237 257
Ampelography: an old technique with future uses
- 44 -
Local names Id. Main name
VVMD7
VVS2
VVMD5
VVMD27
ZAG62
ZAG79
Cabernet sauvignon Ref Cabernet-Sauvignon
237 237
137 151
229 237
175 189
188 194
247 247
Chardonnay Ref Chardonnay
237 241
135 141
231 235
181 189
188 196
243 245
Grenache Ref Grenache
237 241
135 144
223 237
194 194
188 188
257 257
Merlot Ref Merlot
237 245
137 151
223 233
189 191
194 194
259 259
Mourvèdre Ref Mourvèdre
247 247
131 151
223 237
179 189
188 204
251 261
The most influential descriptors
The CA results are shown separately for each of the years studied. The OIV-001, OIV-016, OIV-
151, OIV-230, OIV-231 and OIV-241 descriptors were stable in 2006 and 2007, as well as the
OIV-081-2 descriptors in the year 2007. The first two axes of the correspondence analysis in the
description of the year 2006 explained 52% of the variance (Figure 4). The first axis absorbed
30% of the variance being OIV-225 and OIV-087 the most distinguishing characteristics. The
second axis explained 22% and was related with OIV-084, OIV-053 and OIV-004 descriptors.
OIV-002 OIV-003
OIV-004
OIV-051
OIV-053
OIV-008
OIV-015OIV-067OIV-072OIV-075
OIV-068OIV-080
OIV-081-1 OIV-082
OIV-083-1
OIV-083-2
OIV-079
OIV-081-2
OIV-076
OIV-070
OIV-084
OIV-087
OIV-074
OIV-204
OIV-209
OIV-202
OIV-206
OIV-221
OIV-220OIV-223
OIV-225
OIV-235
OIV-232
OIV-236
OIV-306
OIV-103
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
--axis
F2 (22 %
) --
>
-- axis F1 (30 %) -->
axes F1 and F2: 52 %
Figure 4. CA ordination diagrams of the first two axes of the OIV characters for the vine varieties from the Balearic Islands preserved in Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain): descriptions of 2006
The CA of 2007 showed that variance explained by the first two axes was 49% (Figure 5). Axis
1 explained 28% of the variance and was related with OIV-225, OIV-084, OIV-053 and OIV-004
descriptors. Axis 2 explained 21% of the variance and it was influenced by the OIV-202 and OIV-
070 descriptors. These results were largely in line with the grouping of the varieties in the 2006
and 2007 dendrograms.
Chapter 3
- 45 -
OIV-002
OIV-003
OIV-004OIV-007
OIV-008
OIV-015-2
OIV-051
OIV-053
OIV-067
OIV-068
OIV-070
OIV-072
OIV-074OIV-075
OIV-076
OIV-079 OIV-080
OIV-081-1
OIV-082
OIV-083-1
OIV-083-2
OIV-084
OIV-087
OIV-103
OIV-202
OIV-203
OIV-204
OIV-206
OIV-208
OIV-209
OIV-220
OIV-221
OIV-223
OIV-225
OIV-232
OIV-235
OIV-236
OIV-306
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
-0.6 -0.4 -0.2 0 0.2 0.4 0.6
--axis
F2 (21 %
) --
>
-- axis F1 (28 %) -->
axes F1 and F2: 49 %
Figure 5. CA ordination diagrams of the first two axes of the OIV characters for the vine varieties from the Balearic Islands preserved in Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain): descriptions of 2007
Variations on ampelographic descriptions, agronomic and phenological characters
The differences between 2006 and 2007 ampelographic quantitative dataset were statistically
significant (z-value=2.461, p-value=0.014) in the presence of teeth in the petiole sinus of adult
leaf, being more frequent in 2006 that in 2007 (63% in comparison with 30%). With respect to the
number of the bunch’s wings, there were also statistically significant differences. In the year 2006,
7% of bunches had no wings, while in 2007, there was no bunch without wings (z-value=2.836, p-
value=0.004), whereas in 2007 there were a larger number of bunches with 1 or 2 wings (93%) in
contrast to 2006 (67%; z-value=2.388, p-value=0.016).
The agronomical parameters showed statistically significant differences (Table 4). The number
of bunches per shoot in 2006 was almost double that of 2007 (1.10 compared with 0.65), the vines
had an average of 8 bunches in the year 2006, while in 2007 this was reduced to 6. With regards
to grape production it was greater in 2006 than in 2007 and it dropped from 1.64 kg vine-1 in 2006
to 0.87 kg vine-1 in 2007. However, the number of vine woody shoots per vine and the weight of
the vine shoot also showed statistically significant differences, however in contrast to what
happened with the rest of the parameters, it was greater in 2007 (from 7 to 9 woody shoots per
vine and from 34 to 45 g per woody shoot, respectively). Regarding the quantitative parameters,
relating to the bunch and the berry, showed significant differences in two of them (Table 4), the
length of the stalk and the number of berries per bunch. In both cases, the values were higher for
Ampelography: an old technique with future uses
- 46 -
2006 than for 2007 (4.45 cm vs 3.23 cm and 166 vs 137, respectively). The parameters related
with the characterization of the must (Table 4) showed significant changes in pH and total acidity,
being 2007 samples more acidic. Therefore, they had a lower pH than the 2006 samples.
Phenology showed significant differences with regards to the duration of the vegetative period
only from the time when the hailstorm occurred at which all the plants had the same stage: flower
buds separated (stage H). Until then, for the two years being studied, 18 days had gone by since
the buds had burst (stage C). The differences were marked from that moment until ripeness, and
there were significant differences between the two years studied (t-value=4.491, p-value<0.001),
being the duration of vegetative period longer in 2007 (144 days) than in 2006 (136 days). In 2007
all the varieties had a longer period for phenological stages, since the separated flower buds to
ripeness (t-values>1.706, p-values<0.05).
Table 4. Mean values (± standard error) of qualitative agronomic and enological parameters of the vine varieties from Balearic Islands located at Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain). P-values and degrees of freedom (df) derived from a paired t-test are presented
Variables 2006 2007 t-value p-value df
Yie
ld v
ari
ab
les
Number of bunches per shoot 1.10±0.09 0.65±0.04 4.942 <0.001 26
Number of bunches per vine 8.00±0.62 6.31±0.38 2.284 0.016 25
Number of woody shoots per vine 7.05±0.17 9.27±0.27 8.307 <0.001 26
Yield (Kg grape per vine) 1.64±0.18 0.87±0.09 5.416 <0.001 26
Woody shoot weight (g) 33.83±3.42 45.26±4.41 4.868 <0.001 26
Bunch,
berr
y
Bunch length (stalk excluded) (cm) 16.84±0.58 17.81±0.55 1.702 0.100 26
Bunch width (cm) 10.08±0.25 9.86±0.25 0.850 0.403 26
Stalk length (cm) 4.45±0.22 3.23±0.16 5.501 <0.001 26
Total bunch length (cm) 21.27±0.73 21.05±0.68 0.317 0.754 26
Single bunch weight (g) 285.5±21.0 271.40±23.34 0.598 0.555 25
Berry weight (g) 2.40±0.13 2.50±0.11 1.328 0.196 25
Number of berries per bunch 165.9±15.4 137.2±10.3 2.397 0.012 25
Must
Must yield (%) 27.30±0.88 25.6±1.15 1416 0.169 25
Probable alcohol content (%VOL) 12.88±0.29 12.95±0.25 0.399 0.694 24
pH 3.65±0.04 3.44±0.03 6753 <0.001 24
Total acidity (g L-1 TH2) 4.72±0.21 5.66±0.20 3839 <0.001 24
Ampelographer’s experience and objectivity influenced qualitative ampelographic
descriptions
For the year 2006, out of the 35 characters studied, only 10 (28.6% of the total) not showed
independence between the ampelographer and the description (Table 5), therefore observer’s
experience had an influence over these characters descriptions. The 10 characters were the
anthocyanin colouration of the prostrate hairs of the tip (OIV-003), the colour of the ventral side of
the internodes (OIV-008), the colour of the upper side of the fourth leaf (OIV-051), the shape of
the blade (OIV-067), the number of lobes (OIV-068), the blistering of the upper side (OIV-075), the
shape of the base of the petiole sinus (OIV-080), the number of the bunch wings (OIV-209), the
Chapter 3
- 47 -
juiciness of the flesh (OIV-232) and the firmness of the flesh (OIV-235). However, for the year
2007, the observer’s experience had an influence over the descriptions of 4 characters (11.4% of
the total; Table 5), the anthocyanin colouration of the shoot tip (OIV-002), the colour of the young
leaf’s upper side (OIV-051), the juiciness of the flesh (OIV-232) and the firmness of the flesh (OIV-
235).
Table 5. OIV characters which showed significant differences between the ampelographers and control descriptions in 2006 and 2007 years, for vine varieties from Balearic Islands located at Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain). No significant differences (ns)
2006 2007
Descriptor p (Chi) p (Chi)
OIV-002
ns
<0.001 OIV-003
<0.001
ns
OIV-008
<0.001
ns OIV-051
<0.001
0.002
OIV-067
0,001
ns OIV-068
<0.001
ns
OIV-075
<0.001
ns OIV-080
<0.001
ns
OIV-209
0.020
ns OIV-232
<0.010
0.002
OIV-235 <0.010 <0.001
Discussion
Ampelographic descriptions and molecular analyses have proved to be very useful tools when
describing Vitis vinifera accessions from Balearic Islands. In this study ampelography has been
used as a preliminary technique for the clarification of vegetable material, and the results were
later confirmed by microsatellite analysis. Although the varieties showed a large phenotipical
similarity, they had certain characteristics (OIV-225, OIV-084, OIV-053 and OIV-004) which are
key for their identification (Truel and Boursiquot 1986). Moreover it has been seen that
ampelographic descriptions are influenced by occasional climatic phenomena (hailstorm)
(Dettweiler 1993) which mainly affect the vine shoots, bunches and agronomic characteristics, as
well as the composition of the berry (Calo et al. 1996). The experience of the people who make
the descriptions also has influence on the ampelographic description. In fact, the subjectivity of
certain characteristics, increased by the ampelographer’s lack of experience, is a factor that
complicates the correct description of the plant material (Ortiz et al. 2004).
Differences among varieties using morphological and genetic analysis methods
The morphological grouping of the varieties for which several accessions were studied, as is the
case of Beba blanca, Bobal, Giró, Manto Negro and Quigat showed similarity rates of over 0.90 in
the two studied years. This rate is the minimum for verifying homonyms and for clone selection
processes (Cervera et al. 2002). The slight differences could be related with the variability typical
of the plant material (Bessis et al. 2007), the polyclonal origin of the vine populations (Kozjak et al.
Ampelography: an old technique with future uses
- 48 -
2003), changes in the virus load, epigenetic differences, somatic mutations or several
combinations of these effects (González-Techera et al. 2004). Genetic analyses confirm this
hypothesis, since these accessions showed the same micosatellite profiles. On the other hand,
Callet and Manto Negro varieties are grouped at a similarity level of 0.93 in the two years of the
study however they belong to different varieties according to the microsatellite analysis. Similar
cases have been described in literature and they pointed towards a parentage relationship
between them (Hinrichsen et al. 2001). This hypothesis could be feasible, since due to their
particular geographic location (both are only found in the Balearic Islands), could be the result of
natural crosses involving the same varieties. A similar situation has been described in the Canary
Islands (Spain) for Listan negro which originated from the crossing of two indigenous varieties,
Negramoll and Listán blanco (Zerolo and Cabello 2006). On the other hand, Batista seems to be
different from the rest of the varieties that could be indicating a different genetic origin when
compared with the rest of the varieties studied from the Balearic Islands.
It is remarkable the case of Beba blanca and Beba roja varieties. They have similarity levels of
between 0.81 and 0.90 in the morphological analyses in 2006 and 2007, respectively. According
to Martínez de Toda and Sancha (1997) with similarity rates below 0.85, the varieties could be
considered as different varieties. Since they have identical genotypes, but clear morphological
differences only referred to the colour of the berry (one is white and the other is rose) that could be
explained by the incidence of somatic mutations and therefore should be considered as different
cultivars (Laiadi et al. 2009).
The most influential descriptors
The descriptors that must be included for ampelographic description on this type of plant material
are the colour of the berry (OIV-225), since white and red varieties and one rose variety were
studied, the density of prostrated hairs between the main veins of the adult leaf’s lower side (OIV-
084; Martínez de Toda and Sancha 1997) and the density of the hairs between the main veins of
the young leaf (OIV-053; Allewelt and Detweiler 1989; Martínez de Toda and Sancha 1997), being
the most discriminating characteristics and with a strong influence on the grouping of the varieties
studied.
Variations on ampelographic descriptions, agronomic and phenological characters
The hailstorm phenomenon seems to be the cause of the great influence on the agronomic
characterization parameters, increasing the rainfall in flowering and the damage on the vegetation
that greatly affected the material to be described, however the temperature are largely in line
between 2006 and 2007 data. The parts of the vine most affected by the hailstorm were the young
shoot and the bunch, however the part least affected turned out to be the shoot tip.
Chapter 3
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The leaves showed differences mainly due to the presence of teeth in the petiole sinus. The
characteristics of the presence of teeth were proved to be stable and objective (Ortiz et al. 2004)
and, according to the results of this work, they are easily identifiable irrespective of
ampelographers. Therefore, the statistically significant difference found between the two years of
study could be blamed on the occasional climatic phenomena. Since the hailstorm strongly
reduced the length of the young shoots, when it came to selecting the material to be described,
this came from well-developed side shoots. It seems that this characteristic was found more in
adult leaves of the centre part of the shoots than in well-developed side shoots. In cases of
meteorological accidents, special attention must be paid when making the descriptions of young
vine branches and bunches, because this material is the most damaged.
The hailstorm could change some characters of the bunch like the number of bunch wings, that
in 2007 increased the number of bunches with 2 wings and dropped the number of bunches with 3
wings. This may have been due to either (a) the hailstorm on damaging certain parts of the bunch
increased the visibility of the wings, or (b) because the bunch in its initial stage is capable to
develop secondary growths as occurs in the vine shoots (Pratt 1971). Unfortunately we do not
have evidence of the real causes. The number of grapes per bunch was significantly less in 2007
than in 2006, however the total length of the bunch was similar in 2006 and 2007, on the other
hand, the length of the stalk varied a great deal, as shown by Theiler and Coombe (1985) in
occasional processes of losses of flowers in the flower head, the growth of the stalk of the bunch
stops immediately.
While the variety’s ampelographic characteristics remained more or less constant during the
studied period, the climatic conditions of each year had an influence on the agronomic
characteristics (Santiago et al. 2008). Basically on the yield, as a result of the hailstorm there was
loss of a large number of flower heads like was described by Reynier (2005) and caused by both
the reduction in the number of bunches per vine and the number of berries per bunch, causing the
drastically dropping on yield (kg vine-1) during 2007, similar case was described by Jones and
Davis (2000) where rainfall during physiologically important periods (flowering and maturation)
tended to decrease crop production. However, no significant differences were found in the weight
of the berry like in the study of Jones and Davis (2000). With the vine branches being significantly
damaged, an increase in secondary ramifications was caused and more suckers appeared on the
shoots (Reynier 2005) which caused the weight of pruning wood, the number of vine shoots per
vine and the weight of the vine shoots to increase, this being a sign of energy related to grape
production explained by Rabino et al. (2000).
The hailstorm also caused a delay in phenology that affected the vegetation by prolonging its
vegetative period and affecting the quality of the harvest due to the heterogeneity of the condition
of bunches as was showing by Reynier (2005). With regards to the parameters of the must, the
Ampelography: an old technique with future uses
- 50 -
only value that showed no differences between the years of study was the probable alcohol
content that was more related to stability than to changes in environmental conditions like was
described by Jones and Davis (2000). In this study, acidity and pH were extremely influenced by
the environment, similar results were found by Prenesti et al. (2004). For the years when there is a
delay in phenology, these are years in which the levels of acidity increased as Jones and Davis
described in 2000. The same occurred for the pH data, since these two variables were negatively
related following the results of Zamboni et al. (1997).
Ampelographer’s experience and objectivity influenced qualitative ampelographic
descriptions
All the more discriminating characteristics according to Dettweiler (1993) do not change with the
environment. They are also stable and objective (Ortiz et al. 2004) and, in this study, they were all
established as characteristics about which no differences were found among the ampelographers.
Certain characteristics were more easily assessable than others, because in neither of the two
years of study were found differences among ampelographers when compared with the control
check. With regards to the characteristics about which there do exist differences among the
ampelographers, it should be pointed out that, in the second year of the study, the number of
descriptors about which there were differences dropped considerably. This supports the idea of
Ortiz et al. (2004), which emphasized that the ampelographers’ experience is important, although
the characteristics that define the juiciness of the flesh (OIV-232) and the firmness of the flesh
(OIV-235) are proved to be variable in the two years of study and could be considered subjective.
They are also characteristics that can vary with the period in which the description is made due to
the change in the berry compositions throughout the ripening (Giovanelli and Brenna 2007). The
characteristic of the colour of the upper side of the fourth leaf (OIV-051) even though it is
considered by the OIV (1984) as a characteristic easy to assess, in this work it was not considered
as such, because of the significant differences found when compared with the control check in the
two years studied.
Conclusions
The morphological and molecular characterizations of 27 accessions recollected in Balearic
Islands (Spain) were allowed to validate the ampelographic description method and grouped them
in 17 vine varieties. It has been clear the importance of an accurate selection in the field of
material to be evaluated, since it could to influence some characteristics, especially the presence
of teeth on the adult leaf and the number of the bunch’s wings. The “colour of the upper side of the
fourth leaf” descriptor (OIV-051) should be excluded from the list of characteristics easy to assess
issued by the OIV, since it showed significant differences with the control in both years of study.
Chapter 3
- 51 -
The hailstorm influenced agronomic characterization: reduction in the weight of the bunch,
decrease of the number of berries per bunch, and decrease in the yield (kg vine-1) stands out.
Regarding must quality, the meteorological accidents showed a high influence increasing acidity
and decreasing pH. In addition, the hailstorm influenced the phenology increasing the duration of
the vegetative period.
Ampelographers experience is another factor that could affect the descriptions, but this work
showed that a training process exists. Over two consecutive years of study, the number of
characteristics in which the ampelographers’s differences varied from the control check dropped
considerably.
Morphological evaluations point towards a strong genetic relationship among the studied
accessions. This hypothesis is supported by the common origin of all varieties, since all varieties
are from the Balearic Islands (Spain), this fact could be favoured. In order to corroborate the
possible genetic relationships, further analysis involving larger molecular markers are required.
Finally, the genetic analysis shows Beba blanca as a possible somatic mutant from Beba roja.
Acknowledgements
The authors would like to thank to Thierry Lacombe for critical reading of the manuscript and Josu
G. Alday for statistical advice. Financial support from INIA (Spanish Institute for Agro Food
Research), project RTA 04 175-C3-3 and from ERDF resources.
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Chapter 4
de Maggiolo V. (1535) Mapa del Mediterráneo conposuit hanc cartam in Janua de anno de
1535 die V february
Grape varieties (Vitis vinifera L.) from the Balearic Islands: genetic characterization and relationship with
Iberian Peninsula and Mediterranean Basin
Sonia García Muñoz, Thierry Lacombe, María Teresa de Andrés,
Laura Gaforio, Gregorio Muñoz Organero, Valérie Laucou, Patrice This, Félix Cabello
Genetic Resources and Crop Evolution doi: 10.1007/s10722-011-9706-5
Chapter 4
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Abstract
Ampelographic descriptions, a set of 20 nuclear microsatellite loci (nuSSR), five chloroplast
microsatellites (cSSR), as well as historical references have been used to identify 66 accessions
of Vitis vinifera L. The plant material included major and minor varieties under risk of extinction,
collected in the Balearic Islands, and now conserved in two germplasm repositories site in Spain.
The 66 samples analyzed corresponded to 32 different genotypes, several unique genotypes
were found, three of them remaining unknown. Some synonyms and homonyms were found in the
Mediterranean basin, highlighting that the dispersal of some varieties are related with historical
human movements and migrations occurred in three several periods, (1) around VII century
related to Islam expansion, (2) around XIII-XV centuries and (3) in the XIX century related to
phylloxera crisis.
Some parentages were identified, being the cultivar Callet Cas Concos a key variety in several
crosses, confirming the high value of unknown varieties for parentage analysis. Several grouping
methods confirm the existence of two gene pools.
Key words: nuclear microsatellite, chlorotype, minor varieties, genetic structure, parentage
analysis
Introduction
Grapevine prospection (Vitis vinifera L.) around the world are rescuing varieties under risk of
extinction (Santana et al. 2008; Boursiquot et al. 2009). Rescued plants are preserved in
grapevine collections to prevent genetic erosion (This et al. 2006). It is estimated that there are
approximately 10,000 grapevine cultivars held in germplasm collections worldwide (Santana et al.
2010). As usual for old resources, adaptations or changes of names as well as the lack of
knowledge of ampelography has caused the appearance of many homonyms and synonyms
(Aradhya et al. 2003). Therefore, the vegetal material found among the new prospection has to be
well identified.
In grapevine, the combination of ampelography and microsatellite markers allows the
identification of cultivars facilitating the management of collections (Lopes et al. 1999).
Microsatellites markers are a powerful tool to distinguish cultivars, clarifying synonyms and
homonyms (Lopes et al. 1999; Sefc et al. 2000), and to establish genetic relationships (Boursiquot
et al. 2009; Vargas et al. 2009). In the same way, chloroplast microsatellite markers are useful to
define the direction of the parental crosses (Vargas et al. 2009), and to approach the geographic
origin of the grapevine cultivars (Arroyo-García et al. 2006; Imazio et al. 2006) which is usually
difficult to establish due to the high material exchange (This et al. 2006).
Genetic characterization and relationship with Iberian Peninsula and Mediterranean Basin
- 56 -
The Balearic Islands, located in the Western part of the Mediterranean basin, are composed by
two of the 10 greatest islands in the Mediterranean Sea. The Balearic wines are well known in the
world for their high quality since Roman times (Hidalgo 2002). More recently, wines made from
local Balearic varieties have won several international competitions.
Another important feature of Balearic cultivars is that due to islands isolation, the current gene
pool in the Balearic Islands likely results from plant material exchanges in ports around the
Mediterranean basin and from natural crosses occurring on the islands (Prentice et al. 2003).
Thus, they might be used to disentangle the grapevine movements around occidental
Mediterranean basin.
The Balearic viticulture has changed over the years; first the powdery mildew, afterwards the
phylloxera, and nowadays the homogenization of international wine market have reduced
drastically the genetic pool. In fact, only four varieties (Callet, Manto Negro, Pensal Blanca and
Fogoneu) out of 20 allowed in Quality Demarcations of the Balearic Islands are local varieties, the
rest are internationally spread as Cabernet-Sauvignon, Tempranillo or Chardonnay.
Consequently, the local cultivars are decreasing over the years (This et al. 2006; de Mattia et al.
2007). The loss of cultivars is dramatic in the Balearic Islands since some of the old local cultivars
are only conserved in germplasm repositories. As a consequence, the origins and genetic
relationships of the local cultivars from this area are virtually unknown.
The aim of this work was to characterize the local cultivars growing in the Balearic Islands, and
specifically (i) to clarify cases of synonyms and homonyms of these cultivars with varieties
cultivated in other countries; (ii) attempt to know the geographic movement and migration of these
varieties, and finally (iii) to establish the genetic relationship between them. Microsatellite analysis,
ampelographic descriptions and synthesis of historical references have been used for these
purposes.
Material and Methods
Plant material
We analyzed 66 accessions of Vitis vinifera L. natives from the Balearic Islands (Spain) and
conserved in two different repositories: 33 preserved at the Vitis Germplasm Bank (VGB) “Finca El
Encín” (IMIDRA, Alcalá de Henares, Spain) and 33 preserved in the Germplasm Collection of
Palma de Mallorca (GCPM, Spain; Table 1). The GCPM accessions were collected in Mallorca
Island between 2000 and 2007, whereas the VGB accessions were collected from 1914 to 2000
mainly in the Balearic Islands. However, based on antique bibliography, four accessions of Beba
variety were collected from Levante area and Girona and Pampolat girat variety from Tarragona
(Table 1).
Chapter 4
- 57 -
Table 1. List of the 66 accessions analysed from the Balearic Islands (except * from Levante area, ** from
Girona, *** from Tarragona). List sorted according to their SSR genotypes
G1 Local name
Sample location
2
Berry color
Prime names3 at
VGB “Finca El Encín” INRA GGR Domaine de
Vassal
1 Argamusa VGB white Argamusa Argemusa
2 Batista VGB black Batista Canari
2 Batista GCPM black Batista Canari
2 Batista GCPM black Batista Canari
2 Batista GCPM black Batista Canari
3 Calop VGB white Beba Valenci blanco
3 Calop blanco VGB white Beba Valenci blanco
3 Corazón de Ángel * VGB white Beba Valenci blanco
3 Grumiere blanco * VGB white Beba Valenci blanco
3 Jaumes VGB white Beba Valenci blanco
3 Mateu ** VGB white Beba Valenci blanco
3 Valenci blanco * VGB white Beba Valenci blanco
3 Calop rojo, Calop roig VGB rose Beba rosa –
4 Beba negra, Calop negro, Calop negre
VGB black Valenci Tinto Valensi noir
5 Callet VGB black Callet Callet
5 Callet JB GCPM black Callet Callet
6 Eperó de Gall, Esperó de gall
VGB black Eperó de gall –
7 Excursach, Escursach
VGB black Excursach Murescu
8 Fernandella VGB black Fernandella –
9 Fogoneu VGB black Fogoneu Fogoneu
9 Fogoneu francés VGB black Fogoneu Fogoneu
10 Galmete VGB black Mandón Garro=Mando
10 Sabater GCPM black Mandón Garro=Mando
11 6 GCPM black Mancés de Capdell Mancens=Giro de Baleares
11 Giró VGB black Mancés de Capdell Mancens=Giro de Baleares
11 Giró GCPM black Mancés de Capdell Mancens=Giro de Baleares
11 Mancés de Capdell VGB black Mancés de Capdell Mancens=Giro de Baleares
11 Puig Major GCPM black Mancés de Capdell Mancens=Giro de Baleares
12 Gorgollasa, Gorgollassa
VGB black Gorgollassa Gargollasa
12 Gorgollassa GCPM black Gorgollassa Gargollasa
13 Mansés de tibbus, Mancés de Tibbus
VGB black Mansés de Tibbus –
14 Cabellis VGB black Manto Negro Manto Negro
14 Cabellis GCPM black Manto Negro Manto Negro
14 Manto negro VGB black Manto Negro Manto Negro
14 Manto negro GCPM black Manto Negro Manto Negro
14 Manto negro GCPM black Manto Negro Manto Negro
14 Manto negro GCPM black Manto Negro Manto Negro
14 Manto negro GCPM black Manto Negro Manto Negro
15 Pampolat Girat*** VGB black Pampolat girat Cruixen
16 Massacamps VGB white Quigat Quigat
16 Quigat VGB white Quigat Quigat
17 Sabaté, Sabater VGB black Sabaté Sabaté
18 Viñaté VGB white Viñaté –
18 Vinater blanc GCPM white Viñaté –
19 9 GCPM black Isabelle Isabelle
20 Peu de rata GCPM white Chasselas cioutat Chasselas cioutat
21 Jaumillo GCPM white Santa Magdalena Tempranilla blanca
22 Manto negro GCPM black Tinto Velasco Tinto Velasco
Genetic characterization and relationship with Iberian Peninsula and Mediterranean Basin
- 58 -
G1 Local name
Sample location
2
Berry color
Prime names3 at
VGB “Finca El Encín” INRA GGR Domaine de
Vassal
22 Valent negre GCPM black Tinto Velasco Tinto Velasco
23 Callet Cas Concos GCPM black Callet Cas Concos –
23 Manto negro GCPM black Callet Cas Concos –
23 Planta A GCPM white Callet Cas Concos –
23 Planta B GCPM white Callet Cas Concos –
24 Batista GCPM black Unknown 1 –
25 Callet blanc GCPM white Valent Blanc –
25 Valent blanc GCPM white Valent Blanc –
26 Gafarró GCPM black Gafarró –
27 Giró ros VGB white, black
Giró Ros Giro sardo
27 Giró ros GCPM white, black
Giró Ros Giro sardo
28 Manto negro GCPM black Unknown 2 –
29 Moll GCPM white Pensal Blanca –
29 Pensal blanco, Moll, Prensal blanco
VGB white Pensal Blanca –
30 Manto negro GCPM black Unknown 3 –
31 Boal VGB black Bobal Bobal
31 Vinaté, Vinater VGB black Bobal Bobal
32 Unknow GCPM white Macabeo Macabeu
1 Genotype ( see Table 2)
2 Sample location (Collection maintaining, VGB (Vitis Germplasm Bank “Finca El Encín” (Alcalá de Henares, Spain); GCPM (Germplasm Collection of Palma de Mallorca, Spain))
3 Prime names at Vitis Germplasm Bank “Finca El Encín” (Alcalá de Henares, Spain) and INRA Grape Germplasm Repository of Domaine de Vassal (Marsellan-plage, France), – Variety non present in Vassal collection.
Microsatellite analysis
DNA was extracted from young leaves using the DNeasy Plant kit (QIAGEN, Germany). The set of
20 nuclear microsatellite loci (nuSSR) analyzed was chosen on the basis of their quality and
distribution across the 19 chromosomes of the grapevine genome (Doligez et al. 2006) and was
already used in previous studies (Boursiquot et al. 2009; Vargas et al. 2009; Riahi et al. 2010).
They were amplified in two independent multiplex PCRs as previously described by Ibáñez et al.
(2009). Five chloroplast microsatellites (cpSSRs) were also analyzed using a multiplex PCR
performed according to Ibáñez et al. (2009). Chloroplast haplotypes were named according to
Arroyo-García et al. (2006).
PCR fragments were separated in an AB 3130 automated sequencer and fragments were sized
with GeneMapper 4.0 software using GeneScan-LIZ 500 as internal marker (Applied Biosystems,
Foster City, CA, USA).
Chapter 4
- 59 -
Data analysis
Identifications, synonyms and homonyms
Microsatellite profiles were compared using Excel software first with 1,698 distinct genotypes of
VGB “Finca El Encín”, then with 1,626 different genotypes found in literature and finally with 2,323
distinct genotypes from the INRA Grape Germplasm Repository of Domaine de Vassal (Marsellan-
plage, France, www1.montpellier.inra.fr/vassal). Allele sizes were first standardized to compare all
datasets results using several international varieties as references (This et al. 2004).
Parentage analysis
Parentage analysis was carried out using FaMoz software (Gerber et al. 2000) adapted to grape
(Di Vecchi Staraz et al. 2007) with a discrepancy of three loci. It was evaluated among the 1,698
distinct VGB “Finca El Encín” genotypes and 2,323 distinct Vassal genotypes previously
characterized with the same 20 nuSSR markers. In order to clarify the direction of the parentage
(mother vs father) and the putative geographic origin, the five cpSSRs microsatellites were also
used.
Genetic structure
Standard measures of genetic variation including number of alleles per locus (Na), the observed
(Ho) and expected heterozygosity (He) and the probability of identity (PI) were calculated using
the software IDENTITY v. 1.0 (Wagner and Sefc 1999) on single genotypes. The discrimination
power (D) was calculated according to Tessier et al. (1999).
Different clustering methods were applied to check the consistence of the results. A
dendrogram including 41 cultivars (32 single Balearic genotypes and 9 putative parentages) based
on 20 nuSSR markers was constructed to study genetic relationships using the neighbour-joining
method (Saitou and Nei 1987) and Da genetic distance (Nei et al. 1983). One thousand bootstraps
over loci were performed to assess significance of the tree topology. POPULATIONS software v.
1.2.30 (Langella 2002) was used to represent the genetic similarities between accessions. The
dendrogram was displayed with Tree-View software (Page 1996).
Structure v. 2.1. (Pritchard et al. 2000) based on 19 nuSSRs was applied to identify the genetic
clustering since this program only can be use for unlinked markers (Pritchard et al. 2000). Ten
runs with a burn-in of 50,000 and a run length of 100,000 iterations were performed for a number
of clusters ranging from K=1 to K=10, using the admixture model. The optimal number of genetic
clusters, K, was chosen following the guidelines of Evanno et al. (2005). Each variety was
assigned in one group only if they had more than 70% of the inferred ancestry (Santana et al.
2010). The difference between groups were performed using ARLEQUIN v 3.1
(http://cmpg.unibe.ch/software/arlequin3/) computing FST pairwise using 1,000 permutations.
Those results were tested using FSTAT v. 2.9.3.2
Genetic characterization and relationship with Iberian Peninsula and Mediterranean Basin
- 60 -
(http://www2.unil.ch/popgen/softwares/fstat.htm). This software was also used for allelic richness
(As) calculations. Then, we performed factorial correspondence analysis (FCA) using Genetix v
4.05.2 (Belkhir et al. 1996-2004).
Results
Identifications, synonyms and homonyms
Among the 33 accessions from VGB “Finca El Encín”, 21 different genotypes were identified and
confirmed in Vassal Collection (Table 1). Among the 33 accessions from the Palma Collection, 30
accessions were identified in VGB “Finca El Encín” and confirmed in Vassal Collection (Table 1).
These accessions corresponded to 20 different genotypes (Table 2). Thus, the 66 accessions
represented 32 different genotypes, since some genotypes are located in both collections (Table
2). Seventeen accessions corresponding to 11 different genotypes (34.4%) did not match with any
known genotype in the consulted databases (1,698 genotypes from VGB “Finca El Encín”, 2,323
genotypes from Vassal and 1,626 genotypes from literature). Ampelography, nuSSRs, and
checking the ancient bibliography allowed us to assign a prime variety name for 63 out of 66
accessions, identifying synonyms and homonyms (Table 1); unidentified varieties were named
Unknown 1, Unknown 2 and Unknown 3. The synonyms of Beba and Batista varieties were
discovered based on the literature, and confirmed with ampelographic data from Vassal repository
(Table 1). We found five synonyms between the two collections: Batista (Palma de Mallorca;
Spain) from VGB “Finca El Encín” matched with Canari (Ariège, France) from Vassal; Excursach
(Palma de Mallorca, Spain) with Murescu (Corsica, France); Mansés de Capedell (=Giró; Palma
de Mallorca, Spain) with Mancens (Pyrénées-Orientales, France); Pampolat girat (Tarragona,
Spain) with Cruixent (Corsica, France), and Giró Ros (Felanitx, Manacor; Spain) with Giro sardo
(Sardinia, Italy). Different homonyms were found for Batista, Callet, Mandón, Manto Negro,
Sabaté and Viñaté cultivars, some of them remained unknown (Table 1).
Chapter 4
- 61 -
Table 2. Nuclear SSR data and chloroplast haplotypes of the studied accessions and putative parentages (*) analysed in the VGB “Finca El Encín”
Genotype Prime Name used at
VGB “Finca El Encín” VMC1b11 VMC4f3-1 VVIb01 VVIh54 VVIn16 VVIn73 VVIp31 VVIp60 VVIq52 VVIv37 VVIv67
1 Argamusa 189 189 187 187 290 294 167 169 153 153 263 263 176 190 318 318 88 88 161 165 352 364 2 Batista 167 183 173 206 288 294 165 169 151 153 263 263 180 188 322 322 82 88 163 163 364 375 3 Beba 185 189 187 189 290 294 165 167 151 153 256 263 190 192 318 322 82 84 161 163 366 372 4 Valenci Tinto 185 189 175 187 290 294 165 167 151 153 256 263 190 192 322 322 84 84 152 163 366 372 5 Callet 167 189 175 187 290 290 167 167 151 159 256 263 190 192 318 322 84 88 161 163 372 375 6 Eperó de Gall 189 189 179 187 290 290 167 169 153 159 263 263 176 180 318 322 88 88 163 171 364 372 7 Excursach 185 189 173 175 290 294 167 167 151 151 263 263 190 194 306 318 82 88 163 171 364 375 8 Fernandella 173 185 181 183 290 294 167 169 151 153 256 263 188 192 318 318 82 88 171 171 362 364 9 Fogoneu 189 189 175 206 290 294 167 167 151 159 263 263 190 190 306 322 88 88 161 163 362 375
10 Mandón 173 189 167 206 290 290 167 169 153 159 263 263 176 192 318 326 88 88 163 165 364 372 11 Mancés de Capdell 167 189 189 206 290 290 165 169 151 159 263 263 188 190 318 322 88 88 161 165 362 364 12 Gorgollassa 185 189 167 179 290 290 167 167 153 159 263 263 176 180 318 326 88 88 163 171 358 364 13 Mansés de Tibbus 167 189 167 189 290 294 169 169 151 153 263 263 188 190 318 322 88 88 161 165 364 366 14 Manto Negro 167 189 203 206 290 294 167 167 153 153 256 263 176 176 318 322 84 84 163 177 364 364 15 Pampolat girat 173 173 173 187 290 294 165 167 151 159 263 263 180 188 322 322 84 88 163 177 362 364 16 Quigat 189 194 179 187 290 307 165 167 153 159 263 267 180 190 306 326 84 88 163 165 372 375 17 Sabaté 169 189 203 206 290 294 167 177 151 153 263 263 176 192 318 318 82 84 163 177 358 364 18 Viñaté 167 185 167 187 290 290 140 169 151 153 256 263 176 180 322 322 84 84 163 171 364 368 19 Isabelle 171 177 173 173 294 296 169 169 151 157 263 263 176 180 321 322 82 82 158 171 348 364 20 Chasselas cioutat 173 175 173 179 290 294 165 169 159 159 263 263 182 194 318 322 84 88 152 163 362 364 21 Santa Magdalena 185 185 167 173 288 290 169 175 153 159 256 263 176 194 320 322 84 88 161 177 364 372 22 Tinto Velasco 173 185 187 206 290 290 165 167 151 151 263 263 184 190 322 322 88 88 158 158 358 375 23 Callet Cas Concos 167 189 187 206 290 290 165 167 151 153 256 256 176 192 318 322 82 84 163 171 364 372 24 Unknown 1 167 189 206 206 290 294 165 167 151 153 256 263 176 190 322 322 84 88 161 171 372 375 25 Valent Blanc 189 189 173 206 290 290 167 169 151 159 256 263 176 190 318 318 82 88 165 171 358 366 26 Gafarró 167 189 175 206 290 294 165 167 151 151 256 263 176 190 318 322 84 88 161 171 362 372 27 Giró Ros 167 189 206 206 290 290 167 167 151 151 256 263 176 188 318 322 82 88 171 177 358 366 28 Unknown 2 185 185 183 206 290 290 167 167 151 153 256 263 176 190 322 322 82 84 161 171 358 364 29 Pensal Blanca 185 189 187 203 290 294 169 177 151 153 263 263 176 190 318 326 88 88 161 177 352 366 30 Unknown 3 167 189 203 206 290 294 167 169 153 153 256 263 176 190 318 322 82 84 163 177 364 364 31 Bobal 185 189 173 183 290 294 167 169 151 153 263 263 176 186 326 326 82 84 158 163 358 362 32 Macabeo 185 185 179 187 290 294 167 167 153 153 263 263 176 196 318 326 84 88 158 161 372 375
Genetic characterization and relationship with Iberian Peninsula and Mediterranean Basin
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Genotype Prime Name used at
VGB “Finca El Encín” VMC1b11 VMC4f3-1 VVIb01 VVIh54 VVIn16 VVIn73 VVIp31 VVIp60 VVIq52 VVIv37 VVIv67
* Albaranzeuli bianco 167 189 187 206 290 290 167 169 151 153 263 263 176 188 318 322 84 88 163 177 366 366 * Albillo Mayor 167 185 167 183 290 290 140 167 151 151 256 261 176 180 322 322 84 84 171 171 368 375 * Aspiran 167 185 179 189 290 294 165 169 151 153 263 263 180 188 318 322 88 88 161 163 362 364 * Brustiano faux 173 185 173 179 288 294 167 167 151 153 263 263 196 196 318 322 82 88 158 163 360 375 * Planta Fina de Pedralba 185 185 167 167 290 290 167 169 153 153 263 263 176 190 318 322 88 88 161 177 358 372 * Graciano 173 185 179 206 290 290 167 167 151 159 263 263 180 192 310 318 88 88 165 177 358 366 * Hebén 185 189 167 187 290 290 167 169 153 153 263 263 176 190 322 326 84 88 161 163 364 372 * Legiruela 185 185 173 173 288 294 167 175 153 159 256 256 176 194 322 322 84 88 161 177 364 364 * Monastrell 173 189 179 179 290 290 167 167 153 159 263 263 180 192 318 322 88 88 165 171 358 364
Genotype Prime Name used at
VGB “Finca El Encín” VVMD21 VVMD24 VVMD25 VVMD27 VVMD28 VVMD32 VVMD5 VVMD7 VVS2 Haplotype
1 Argamusa 243 249 208 212 239 239 181 194 260 260 250 270 223 231 237 237 131 135 A 2 Batista 249 255 208 212 247 253 179 189 250 260 250 270 229 235 237 251 141 151 A 3 Beba 249 255 208 210 253 253 181 189 246 260 254 270 233 237 241 247 133 141 A 4 Valenci Tinto 249 255 208 210 253 253 181 189 260 262 238 254 225 233 237 241 135 141 D 5 Callet 255 255 212 218 239 253 181 189 238 260 238 238 233 237 237 247 131 141 A 6 Eperó de Gall 243 249 208 210 239 239 179 181 260 260 238 270 223 231 241 247 131 144 A 7 Excursach 243 249 210 218 239 239 181 181 246 260 254 270 223 237 237 237 141 144 D 8 Fernandella 243 249 208 210 237 239 181 181 246 260 248 260 225 225 231 247 131 144 D 9 Fogoneu 243 255 218 218 239 239 179 181 238 246 238 270 233 237 237 245 131 144 D
10 Mandón 249 249 208 210 253 261 183 194 246 260 238 270 223 237 237 237 141 151 A 11 Mancés de Capdell 243 255 208 218 239 247 179 181 238 260 238 260 223 233 245 247 131 131 D 12 Gorgollassa 249 249 208 210 253 261 179 194 236 260 238 270 219 237 237 247 141 151 A 13 Mansés de Tibbus 243 243 218 218 239 261 181 181 260 260 260 270 219 223 241 247 131 151 A 14 Manto Negro 243 255 210 218 239 239 181 194 246 260 254 270 231 233 237 241 131 144 A 15 Pampolat girat 249 249 212 216 253 253 181 183 246 246 254 254 233 237 241 247 131 141 A 16 Quigat 249 249 208 210 239 253 181 181 236 260 238 254 229 231 241 247 144 151 A 17 Sabaté 243 255 212 218 239 239 185 194 246 260 250 270 225 231 237 241 131 131 D 18 Viñaté 243 253 208 214 239 239 183 194 260 260 254 270 229 231 237 241 144 144 A 19 Isabelle 249 249 208 208 239 247 179 183 228 238 246 270 235 235 233 247 121 151 B 20 Chasselas cioutat 249 265 208 212 239 253 185 189 220 270 238 238 225 233 237 245 131 141 B 21 Santa Magdalena 253 255 208 210 237 239 185 194 236 260 250 270 225 235 237 245 131 144 A 22 Tinto Velasco 255 255 208 214 237 237 179 185 250 262 250 250 229 235 231 251 131 131 A 23 Callet Cas Concos 243 255 210 212 239 253 181 189 260 260 238 254 231 233 237 247 141 144 A
Chapter 4
- 63 -
Genotype Prime Name used at
VGB “Finca El Encín” VVMD21 VVMD24 VVMD25 VVMD27 VVMD28 VVMD32 VVMD5 VVMD7 VVS2 Haplotype
24 Unknown 1 243 255 212 218 239 239 181 181 246 260 238 270 233 237 245 247 144 144 A 25 Valent Blanc 249 249 208 212 253 253 179 181 246 260 270 270 223 237 237 237 141 141 A 26 Gafarró 255 255 212 218 239 253 179 181 246 260 238 238 231 237 237 247 131 141 A 27 Giró Ros 243 249 208 212 239 253 179 181 246 260 238 270 231 237 237 237 141 144 D 28 Unknown 2 243 249 208 218 237 239 181 183 238 260 270 270 225 237 237 241 131 144 D 29 Pensal Blanca 243 255 208 212 239 253 179 194 260 262 250 254 231 231 237 241 135 141 A 30 Unknown 3 243 255 208 212 239 253 181 194 246 260 254 270 231 233 237 241 139 141 † 31 Bobal 243 243 208 210 239 265 181 189 236 262 248 270 225 231 237 241 144 146 A 32 Macabeo 243 253 208 210 237 239 189 194 238 260 248 254 233 233 237 237 131 144 A
* Albaranzeuli bianco 243 249 208 208 239 253 179 194 236 246 238 270 231 233 237 241 141 144 A * Albillo Mayor 247 253 214 218 239 253 183 194 236 260 250 270 229 233 237 251 141 144 A * Aspiran 249 255 208 212 239 247 179 181 230 260 238 260 223 223 245 247 131 131 D * Brustiano faux 249 253 208 208 237 239 181 189 238 260 248 254 233 233 237 237 131 144 A * Planta Fina de Pedralba 249 253 210 218 239 253 179 194 250 260 248 270 225 237 237 241 141 144 A * Graciano 249 253 208 208 261 269 179 183 246 260 238 254 223 235 237 237 137 151 A * Hebén 243 249 208 210 239 253 181 194 236 260 254 270 231 237 237 241 141 144 A * Legiruela 249 255 208 208 237 247 185 189 236 246 250 270 225 235 231 245 131 155 A * Monastrell 243 249 208 218 239 261 179 189 246 260 238 254 223 237 247 247 131 151 A
† missing data
Genetic characterization and relationship with Iberian Peninsula and Mediterranean Basin
- 64 -
Parentage analysis
The 32 single genotypes were analyzed for possible parent-offspring relationships. We obtained
13 putative parentages (i.e. father-mother-son; Table 3), six of them with full compatibility for all
nuclear microsatellites studied and for the chlorotype of both parents (Callet, Eperó de Gall,
Gafarró, Manto Negro, Unknown 1 and Viñaté). Five oriented crosses were revealed. In the case
of pedigrees not resolved with chlorotype data, the female status of Hebén and Excursach
varieties determined the mother for six other pedigrees, two of them showed also a bootstrap
value above 70% in the dendrogram. Actually, two lineages (kin-groups) were pointed out, one
related with Callet Can Concos variety and one with Hebén variety (French synonym: Gibi; Figure
1).
Table 3. Thirteen putative parentages. LOD scores obtained using FaMoz software. Capital letters in brackets correspond to the haplotype defined by Arroyo-García et al. (2006, Table 2). The female parentage might be identified when both parentages showed the same haplotype and the variety is female
Progeny Parent 1 Parent 2 Consistent
loci LOD score
Callet (A) Callet Cas Concos (A) Fogoneu (D) 20/20 28.31 Callet Cas Concos (A) Beba (A) Giró Ros (D) 19/20 27.50 Eperó de Gall (A) Hebén (A)* Monastrell (A) 20/20 23.65 Fogoneu (D) Excursach (D)* Mansés de Capdell (D) 19/20 27.62 Gafarró (A) Callet Cas Concos (A) Fogoneu (D) 20/20 23.77 Giró Ros (D) Valent Blanc (A) Albaranzeuli bianco (A) 20/20 25.87 Gorgollassa (A) Hebén (A)* Monastrell (A) 19/20 23.20 Macabeo (A) Hebén (A)* Brustiano faux (A) 19/20 26.42 Mandón (A) Hebén (A)* Graciano (A) 19/20 25.04 Manto Negro (A) Callet Cas Concos (A) Sabaté (D) 20/20 46.82 Santa Magdalena (A) Planta fina de Pedralba (A) Legiruela (A) 19/20 24.58 Unknown 1 (A) Callet Cas Concos (A) Fogoneu (D) 20/20 42.67 Viñaté (A) Hebén (A)* Albillo Mayor (A) 20/20 47.36
* Female variety
Lineage 1
Lineage 2
Figure 1. Lineages and parentages proposed for the Balearic Islands varieties using 20SSRs. Only the most reliable parentages are shown
Callet Cas Concos
Beba
Unknow 1 Callet Gafarró
Giró Ros
Fogoneu
Excursach
Mansés de Capdell
Manto Negro
Sabaté
Brustiano faux
Albillo Mayor
Hebén
Graciano
Monastrell
Gorgollassa
Eperó de Gall
Mandón
Macabeo
Viñaté
Chapter 4
- 65 -
Genetic structure
All microsatellite markers were polymorphic with a number of alleles per locus ranging from 3 to
10 (Table 4). A total of 136 alleles and a mean of 6.8 alleles per locus were found. The mean
discrimination power per locus was 80.3%, although VMC4f3-1 showed the highest discrimination
power (D=93.6%) and VVIn73 the weakest (D=11.9%; Table 4). The cumulative probability to find
different individuals with the same profile for each of the 20 nuSSR was 2.322 x 10-19. In this
study, haplotype A (67.7%), B (6.5%) and D (25.8%) were found (Table 2).
Table 4. Genetic diversity among 32 non redundant accessions. Mean ± standard deviation
Locus Na1 He
2 Ho
2 D
3 PI
4
VMC1b11 10 0.740 0.750 0.850 0.104
VMC4f3-1 10 0.850 0.875 0.936 0.039
VVIb01 5 0.500 0.656 0.596 0.324
VVIh54 6 0.648 0.688 0.811 0.179
VVIn16 4 0.647 0.719 0.768 0.198
VVIn73 3 0.382 0.438 0.119 0.444
VVIp31 10 0.796 0.938 0.896 0.068
VVIp60 6 0.653 0.625 0.797 0.184
VVIq52 3 0.626 0.594 0.789 0.213
VVIv37 7 0.808 0.906 0.912 0.063
VVIv67 9 0.813 0.938 0.920 0.057
VVMD21 5 0.703 0.656 0.834 0.147
VVMD24 6 0.752 0.906 0.820 0.102
VVMD25 6 0.648 0.594 0.832 0.177
VVMD27 6 0.755 0.844 0.883 0.091
VVMD28 9 0.699 0.813 0.781 0.123
VVMD32 7 0.768 0.781 0.850 0.089
VVMD5 8 0.844 0.875 0.926 0.044
VVMD7 7 0.718 0.813 0.846 0.119
VVS2 9 0.775 0.813 0.895 0.086
Mean 6.8 ± 2.2 0.706 ± 0.111 0.761 ± 0.133 0.803 0.142
Cumulative 136 0.999999999994 2.322 x 10-19
1 Number of allele per locus (Na)
2 Expected (He) and observed heterozygosity (Ho)
3 Discrimination Power (D)
4 Probability of identity (PI)
The dendrogram revealed 10 bootstrap values above 70%. Four main groups labeled A, B, C
and D were found (Figure 2, Table 5). Clusters A and B mainly grouped all the samples related
with Callet Cas Concos variety (Lineage 2; Figure 1). Most of these varieties carried chlorotype A,
but five (Giró Ros, Sabaté, Excursach, Valenci Tinto and Fogoneu) showed chlorotype D. In these
groups only one bootstrap value was consistent (>70%) with the parent relationship found with
FaMoz (Valent blanc - Giró Ros). Clusters C and D grouped the other samples, most of them
related with Hebén variety (Lineage 1; Figure 1). The varieties carrying chlorotype A were, again,
the most frequent ones, although four varieties (Unknown 2, Fernandella, Mancés de Capdell and
Genetic characterization and relationship with Iberian Peninsula and Mediterranean Basin
- 66 -
Aspiran) showed chlorotype D, and other two varieties carried chlorotype B (Chasselas cioutat
and Isabelle). In these groups, four bootstrap values were higher than 70% at terminal nodes and
showed parent relationship consistent with FaMoz results (Macabeo – Brustiano faux, Viñaté –
Albillo Mayor, Gorgollassa – Mandón, and Santa Magdalena – Legiruela).
Table 5. Assignments of the unique genotypes of the cultivars and proposed parentages (*) following the Dendrogram and Structure cluster assignment (>70% ancestry) results
Genotype Prime Name used at VGB
“Finca El Encín”
Cluster assignment
Dendrogram group
Structure cluster assignment
1 Argamusa A
2 Batista D 1
3 Beba B 3
4 Valenci Tinto B
5 Callet B 3
6 Eperó de Gall D 2
7 Excursach B
8 Fernandella D 1
9 Fogoneu B 3
10 Mandón D 2
11 Mancés de Capdell D
12 Gorgollassa D 2
13 Mansés de Tibbus D
14 Manto Negro A 3
15 Pampolat girat D
16 Quigat D
17 Sabaté A
18 Viñaté C 1
19 Isabelle D 1
20 Chasselas cioutat D 1
21 Santa Magdalena D 1
22 Tinto Velasco D 1
23 Callet Cas Concos B 3
24 Unknown 1 B 3
25 Valent Blanc A
26 Gafarró B 3
27 Giró Ros A 3
28 Unknown 2 C
29 Pensal Blanca A
30 Unknown 3 A 3
31 Bobal C 1
32 Macabeo C 1
* Albaranzeuli bianco A
* Albillo Mayor C 1
* Aspiran D
* Brustiano faux C 1
* Planta Fina de Pedralba C
* Graciano D
* Hebén C
* Legiruela D 1 * Monastrell D 2
Chapter 4
- 67 -
0.1
Albaranzeuli bianco A
Valent Blanc A
Giró ros D73
33
Argamusa A
Pensal Blanca A72
Sabaté D
Manto negro A
Unknown 361
43
14
3
Excursach D
Callet Cas Concos A
Beba A
Valenci Tinto D92
29
Callet A
Gafarró A48
Fogoneu D
Unknown 1 A41
19
3
1
Bobal A
Macabeo A
Brustiano faux A94
17
Hebén A
Planta Fina A48
Unknown 2 D
Viñaté A
Albillo Mayor A99
32
2
0
Eperó de Gall A
Quigat A37
Mandón A
Gorgollasa A76
Monastrell A
Graciano A74
47
14
Fernandella D
Mansés de Tibbus A
Mancés de Capdell D
Aspiran D62
55
15
Pampolat girat A
Chasselas cioutat B62
Batista A
Isabelle B70
Tinto Velasco A
Santa Magdalena A
Legiruela A98
26
11
5
1
0
0
0
Figure 2. Neighbour-joining dendrogram of genetic relationship among the 32 varieties investigated and their putative parent relationship. Results calculated with Da genetic distance (Nei et al. 1983). Capital letter at the end of the varietal name is the Chlorotype according to Arroyo-García et al. (2006; Table 2)
C
A
B
D
Genetic characterization and relationship with Iberian Peninsula and Mediterranean Basin
- 68 -
0% 20% 40% 60% 80% 100%
Unknow 1
Gafarró
Callet
Fogoneu
Callet Cas Concos
Beba
Manto Negro
Unknown 3
Giró Ros
Valenci Tinto
Excursach
Albaranzeuli bianco
Mancés de Capdell
Sabaté
Pensal Blanca
Valent Blanc
Mansés de Tibbus
Argamusa
Hebén
Eperó de Gall
Monastrell
Mandón
Gorgollassa
Graciano
Planta Fina
Aspiran
Quigat
Unknow 2
Pampolat girat
Viñaté
Macabeo
Fernandella
Bobal
Batista
Chasselas cioutat
Santa Magdalena
Albillo Mayor
Brustiano faux
Tinto Velasco
Legiruela
Isabelle
Cluster 1 Cluster 2 Cluster 3
Figure 3. Proportions of ancestry of individual genotypes at 19 SSRs in each of K=3 genetic clusters estimated with Structure software
Chapter 4
- 69 -
Structure clustering was applied to confirm these previous results. The optimal number of
genetic clusters was K=3 (Ln P(D)=-2287.10; ΔK=24.21). Cluster 1 (12 genotypes) and Cluster 2
(four genotypes) were included in the Clusters C and D found in the neighbour-joining tree. All
varieties included in the Cluster 2 but Monastrell shared the variety Hebén as female parent.
Cluster 3 (nine genotypes) were distributed among the groups A and B of the dendrogram,
grouping the cultivars related with Callet Cas Concos variety, since they shared the half of their
alleles in all the loci studied, confirming first-degree genetic relationships. Sixteen varieties were
unassigned (Figure 3; Table 5). The genetic diversity parameters studied (Na, He, and As) for
clusters characterization were higher for Cluster 1 (Table 6) being the most heterogeneous group.
All differentiation tests among pairs of clusters were significant (p<0.001) being the greatest
differences between Clusters 2 and 3 (FST=0.140; Table 7). This was also confirmed by
multivariate analysis (FCA). All varieties were assigned in the original group established with
Structure software (Figure 4).
Table 6. Genetic diversity among the 3 Clusters for 20 SSR identified with Structure software. Mean ± standard deviation
n1 Na2 He3 Ho3 As4
Cluster 1 12 6.0 ± 1.8 0.728 ± 0.098 0.750 ± 0.151 4.09 ± 0.83 Cluster 2 4 2.8 ± 1.0 0.506 ± 0.233 0.725 ± 0.352 2.80 ± 1.03 Cluster 3 9 3.4 ± 1.0 0.611 ± 0.098 0.802 ± 0.131 3.00 ± 0.70
1 Number of varieties included in each cluster (n)
2 Number of allele (Na)
3 Expected (He) and observed (Ho) heterozygosity
4 Allele richness (As)
Table 7. Pairwise FST values for the Clusters identified with Structure software (above the diagonal) and p values (in the lower triangle) obtained with ARLEQUIN and tested with FSTAT
Cluster 1 Cluster 2 Cluster 3
Cluster 1 _ 0.095 0.079 Cluster 2 <0.001 _ 0.140 Cluster 3 <0.001 <0.001 _
Genetic characterization and relationship with Iberian Peninsula and Mediterranean Basin
- 70 -
Figure 4. Factorial component analysis based on the genetic clusters estimated with Structure
Discussion
Identifications, synonyms, homonyms
The identical genotypes found in this work could be strongly considered as synonyms since the
cumulative probability of identity is around 10-19 (Sefc et al. 2000). We confirmed the synonym
between Batista and Canari found by Truel (1985) and found four other new synonyms for
Excursach, Mansés de Capdell, Giró Ros, and Pampolat girat matching with varieties located
around the Mediterranean basin. The varieties Unknown 2 and Unknown 3 have different
genotypes, although they were named Manto Negro, confirming that under the Manto Negro
designation were identified several varieties (Oliver Moragues 2000). These findings corroborate
that the mistakes are very common as a consequence of the high number of cultivated grape
varieties and variability of ampelographic characters (Vouillamoz et al. 2004; Martínez et al. 2009).
The Beba variety is usually used as white table grape. It shows a high number of synonyms
being Calop the most antique (Martí 1978). Other synonyms have been found as Ain el Kelb and
Tebourbi (Laiadi et al. 2009) or Panse de Provence (Truel 1985), confirming the high historic value
of this variety through time. There is a rose mutation of Beba, called Calop rojo and Calop roig.
However, between the white and rose accessions of Beba variety no differences were found either
with the nuSSRs nor with ampelographic descriptions except the berry colour (Chapter 3). Thus
Beba rose is a somatic mutation of the white Beba and they could be considered as different
cultivars of the same genotype.
The Batista variety is present in Spain only in the Balearic Islands but it was found in the South
Western of France and in Italian Alpes under Canari and Luverdon names (Schneider et al. 2001).
Cluster 2
Cluster 1
Cluster 3
Chapter 4
- 71 -
Pampolat girat is found in the Balearic Islands, Valencia and Catalonia, at least, since 1845
(Odart 1845) and it was found in Corsica too (France). Its synonym Cruixent is present in the
Luxembourg Catalogue (Viala and Vermorel 1910). Whereas Excursach is a female variety found
in the Balearic Islands almost since 1842 (Ferrer 1999). Its synonym Murescu (vs Muresca) has
been mentioned in Corsica in 1822 (CIVAM 1992).
The first reference found for the Giró variety corresponds with a map of the Balearic Islands
(Despuig 1784). Under the names of Giró two different varieties are known, with different
genotype, the black varieties Mansés de Capdell (syn. Giró and Mancens) and Giró Ros (syn. Giró
sardo), although a white cultivar was found for Giró Ros (syn. Giró sardo) in the Balearic Islands.
Our results showed that Giró Ros have the same genotype for white and black cultivars, it means
that a somatic mutation exist, therefore they could be considered as different cultivars.
The Mansés de Capdell (syn. Giró and Mances) variety was present in the Balearic Islands and
Catalonia as Mancés, Mansés (Carretero 1875) or Mancesa (Abela y Sáinz de Andino 1885), and
in the French Oriental Pyrenees as Mancens (Truel 1985). The Giró Ros (syn Giró sardo) variety
was present in the Balearic Islands (Estelrich 1903) and in Sardinia (Italy; Ottavi 1868).
Geographical distribution
The synonyms and homonyms are the result of migration events and cultural exchanges (Labra et
al. 2002), lack of ampelographic knowledge and adaptation or substitution of the native names in
different languages for the unfamiliar varieties (Aradhya et al. 2003). Therefore, the geographical
origin of the grapevine is complex to establish (This et al. 2006), although the haplotypic
distribution on a large sample is a useful tool to suggest grapevine origin (Imazio et al. 2006).
Haplotypes percentage found in this work was similar to the one described by Arroyo-García et al.
(2006); they found that chlorotype A was the most frequent one in the cultivated grapevine in the
Central and West part of the Mediterranean basin; whereas the clorotype B was the least frequent
one. The Excursach, Mansés de Capdell and Giró Ros varieties might have a Greek ancestral
attending to their chlorotypes. They showed chlorotype D, the most frequent one in Greece
(Arroyo-García et al. 2002) and in the Italian and Balkan Peninsulas (Arroyo-García et al. 2006).
This could explain the high assignment rate from Greek cultivars to Spanish ones found by Sefc et
al. (2000).
The migration of some of the studied varieties seems to have occurred in three different
periods. The first one could be considered around VII century with the expansion of Islam.
Although the origin of Beba variety is unknown, the oriental or North African origin has been
suggested (Laiadi et al. 2009). This variety is present in Spain especially around the
Mediterranean coast (Valcárcel 1791) as well as in Algeria (Pulliat 1898). The etymology of the
Beba name in Spanish could come from Arabic or Hebrew (Rojas Clemente 1879). However a
Genetic characterization and relationship with Iberian Peninsula and Mediterranean Basin
- 72 -
Spanish origin of this variety in Algerian viticulture has been suggested by Pulliat (1898) under the
name of Valenci, which remember the name of Valencia (Spain). This theory is possible as a
consequence of the important emigration from the Balearic Islands to Algeria after the phylloxera
crisis, during the XIX century (Oliver Fuster 1980). The Excursach variety could be connected with
Moors (CIVAM 1992) supported by the female ancestral trait of Excursach. This variety is also
found in Corsica, so it might be spread by Moors both in Corsica and the Balearic Islands, giving
birth to Fogoneu in the Spanish islands, which is not present in France or Italy. An alternative
explanation suggests that the Spanish could have carried or brought this variety from or to
Corsica, during XIII-XV centuries, when Corsica, Sardinia and the Roussillon were under the
Kingdom of Aragón (current Spain). This last theory could also be proposed for Pampolat girat,
although the Corsican name Cruixent, written with “x”, letter rarely included in Corsican and
French alphabets but frequent in Catalonian one, make us to hypothesize that this variety could
have been carried from Spain to Corsica. The Mansés de Capdell (syn. Giró and Mances) variety
seems to be related too with Fogoneu; hence we think that this variety was first in Spain and then
carried to Oriental Pyrenees. The Giró Ros (syn Giró sardo) origin is uncertain; it could have been
first in Spain and then brought to Sardinia when they were under Kingdom of Aragón (current
Spain) since this variety is related with Callet Cas Concos variety. This theory is supported by the
ancient trait of the color mutation found for this cultivar, as well as the Spanish origin of this
cultivar in Sardinia has been previously mentioned (OIV 1961). The influence of the Spanish in
Sardinian viticulture has been described by de Mattia et al. (2007), given that the Sardinian
cultivars are related with Italian and Spanish varieties (Grassi et al. 2003). However, attending to
its name (Sardo, from Sardinia) it could have been first present in Sardinia and then brought to the
Balearic Islands.
Batista origin is uncertain although it seems to have a French origin (Schneider et al. 2001).
Nowadays this variety is present in Spain only in the Balearic Islands. It might be carried from
France to Catalonia and then to the Balearic Islands when the Roussillon was under the County of
Catalonia (current Spain) around the XII century. Figure 5 summarize those entire hypotheses.
Parentage analysis
The bibliography confirmed the coexistence between the proposed parental. The cpSSRs are
maternally inherited in grapevine (Arroyo-García et al. 2002) revealing the direction of the cross.
Only in Giró Ros=Valent Blanc x Albaranzauli bianco an incompatibility of chlorotype was found. In
one case, Santa Magdalena=Planta fina de Pedralba (syn. Farana, Mayorquin) x Legiruela (syn.
Prié blanc, Agostenga), all the cultivars showed the same chlorotype, therefore the female and
male parents could not be specified; the bootstrap value of the dendrogram suggested a strong
genetic relationship between the cultivars. Other direct relationships for Legiruela (Albillo Real and
Luglienga bianca) have been described (Schneider et al. 2010). On the basis of LOD score and
Chapter 4
- 73 -
the historical references the parentage Fogoneu=Excursach x Mansés de Capdell is the most
likely; although Aspiran variety is related with Mansés de Capdell as they shared half of their
alleles.
Figure 5. Theories about the movement of the studied cultivars through history (explained in the text)
Several crosses were inconsistent with one nuSSR marker since a discrepancy of three loci
was fixed to assign the parentage allowing possible mistakes, which may be due to the presence
of null alleles or mutations (Boursiquot et al. 2009, and references therein). We obtained several
putative parentages, six of them with full compatibility for all nuclear microsatellites studied and for
the chlorotype of both parents, sustaining the consistency of the suggested pedigrees.
Genetic structure
The VMC4f3-1and VVIn73 nuSSRs have been already described to be respectively the most and
least informative ones by other authors (El Oualkadi et al. 2009; Riahi et al. 2010) since a higher
discrimination power (D) implicates a lower probability of confusion of cultivars (Sefc et al. 2000).
The expected heterozygosity is a useful measurement to compare with other works since the
number of allele per locus is sensitive to the number of cultivars analyzed (Sefc et al. 2000;
Aradhya et al. 2003). The genetic diversity found in this work (70.6%) was similar to the one
obtained in studies with analogous sample size (de Mattia et al. 2007; Santana et al. 2008).
The different clustering methods used are consistent with the existence of at least two lineages
in the analyzed samples. The greatest differences between Clusters 2 and 3 are justified since the
Cluster 2 could perform the first gene pool, since the most of the varieties are related with Heben
variety, and Cluster 3 could perform the second one, being Callet Cas Concos the key variety.
This cultivar is related with two of the most important wine varieties in the Balearic Islands (Callet
and Manto Negro).
Genetic characterization and relationship with Iberian Peninsula and Mediterranean Basin
- 74 -
Conclusions
Material exchanges of grapevine are related to historical migrations of civilizations consequently
giving several synonyms around the Mediterranean basin. The geographic distribution proposed
for the varieties from the Balearic Islands are consistent with their historical references.
Prospection are useful and interesting for the discovery of new genotypes and they are also
needed in order to ensure the long-term survival of the found cultivars. The finding of Callet Cas
Concos and Hebén varieties was the key for clarifying several crosses. Unknown varieties, under
risk of extinction, are putative options for breeder, for the wine industry and could clarify new
genetic relationships. Finally, the combination of ampelography, microsatellite analysis and
synthesis of historical references of the cultivars has shown to be excellent tools for a good
identified grapevine material and to establish a correct regional viticulture.
Acknowledgements
Financial support from INIA, project RTA 04 175-C3-3 and from ERDF resources. Sonia García
Muñoz was supported by a PhD scholarship from INIA “Caracterización y selección de variedades
de vid de Baleares”.
We would like to thank Ana I. de Lucas for the first critical reading of the manuscript; INRA
Montpellier (France) to welcome Sonia García-Muñoz for a short stay, as well as Anna Schneider
(Istituto di Virologia Vegetale, Grugliasco, Torino, Italy) who compared the microsatellite data
through her Italian minor varieties database. The authors wish to thank Antoni Martorell Nicolau
for the localization of minor cultivars.
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Chapter 5
Danger
(This photo is courtesy of Gregorio Muñoz Organero)
Evaluation of susceptibility to powdery mildew (Erysiphe necator) in Vitis vinifera varieties
Laura Gaforio, Sonia García Muñoz, Félix Cabello,
Gregorio Muñoz Organero
Accepted in Vitis (2011)
Chapter 5
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Abstract
Susceptibility to grape powdery mildew (Erysiphe necator Schwein.) was studied in 159 Vitis
vinifera foreign and native grape varieties grown in Spain. The relationship between morphological
features of vines and their susceptibility to the disease was also studied. The infection was
evaluated under natural conditions on leaves and bunches. A total of 35 cultivars were very
susceptible to the disease (very low to low resistance on bunches), while another 83 showed low
susceptibility (high to very high resistance on bunches). Results provide useful information for
grape growers and breeders for the selection of varieties less susceptible to powdery mildew.
Key words: Erysiphe necator, morphology, Oidium, susceptibility, Vitis vinifera
Introduction
Fungal diseases are a major problem in the cultivation of grapevine, and one of the most
threatening pathogens is the fungus Erysiphe necator Schwein., the casual agent of powdery
mildew. This biotrophic ascomycete invades host epidermal cells and colonizes leaves, rachis,
and grapes, causing a decrease of vine growth, yield, and quality of grapevine production (Pool et
al. 1984, Calonnec et al. 2004). The incidence of powdery mildew has increased in recent years in
Europe. Climatic conditions and reduced efficacy of fungicides have been suggested as possible
reasons (Staudt 1997). Fungicide treatments increase economic costs and negatively affect the
environment. Furthermore, fungal strains are developing resistance to some commonly used
fungicides (Savocchia et al. 2004). Thus, the possibility of selecting less-susceptible, high-quality
cultivars is an alternative management strategy of great importance. Although the most commonly
cultivated species, Vitis vinifera, has proved to lack resistance to powdery mildew, the degree of
susceptibility varies with the cultivar and the environmental conditions (Li 1993; Péros et al. 2006).
The aims of the present study were to analyse the susceptibility of 159 cultivars of Vitis vinifera
to powdery mildew and to determine whether morphological features may influence this response
on the vine.
Material and Methods
Material
This study was conducted for four years (2006-2009). Vines were located in the Vitis Germplasm
Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain). Some clones of each variety were
studied to determine the degree of susceptibility to powdery mildew, resulting in 473 samples from
159 cultivars (2–7 clones per cultivar, 4 plants per clone; Table1).
Evaluation of susceptibility to powdery mildew
- 80 -
Table 1. Modal data of maximum degrees of resistance to powdery mildew on bunches (B) and leaves (L) of Vitis vinifera cultivars, according to OIV descriptors 455 and 456: l (low)=1, 3; m (medium)=5; h (high)=7, 9. The cultivars are listed in order based on the resistance to the fungus on bunch, following by the resistance on leaf and alphabetical order. The varieties collected in the Balearic Islands are in bold
Variety B L Variety B L Variety B L
Beba l l Doña Blanca m m Listán Negro h m
Benedicto de Aragón l l Garnacha Roja m m Mantúo h m
Brancellao l l Garrido Fino m m Mantúo de Pilas h m
Cabernet-Sauvignon l l Garrido Macho m m Merseguera h m
Castellana Blanca l l Graciano m m Monastrell h m
Espadeiro l l Listán del Condado m m Moravia Agria h m
Forastera l l Macabeo m m Morenillo h m
Garnacha Blanca l l Merenzao m m Moristel h m
Garnacha Tintorera l l Morate m m Moscatel de Angüés h m
Gualarido l l Puesto Mayor m m Negramoll h m
Cayetana Blanca l l Rojal Tinta m m Pardillo h m
Malvasía Aromática l l Rufete m m Rayada Melonera h m
Marfal l l Savagnin Blanc m m Sinsó h m
Mazuela l l Tetona m m Sousón h m
Mencía l l Treixadura m m Tinto Velasco h m
Moscatel de Grano Menudo l l Verdejo m m Tortosí h m
Parraleta l l Verdejo Tinto m m Trepat h m
Planta Fina l l Bastardo Negro m h Vijariego Blanco h m
Rocía l l Bobal m h Xarel.lo Rosado h m
Salceño Blanco l l Excursach m h Albillo Real h h
Sumoll l l Fernandella m h Blanquiliña h h
Tempranillo l l Gabriela m h Carrasquín h h
Verdil l l Tarragoní m h Cuatendrá h h
Vidadillo l l Alcañón h l Doradilla h h
Benedicto l m Batista h l Eperó de Gall h h
Cariñena Blanca l m Cabernet Franc h l Fogoneu h h
Garnacha Tinta l m Derechero h l Forcallat Tinto h h
Godello l m Garnacha Peluda h l Gorgollassa h h
Morisca l m Grumet h l Legiruela h h
Palomino l m Hebén h l Listán Prieto h h
Palomino Fino l m Malvar h l Loureira h h
Picapoll l m Maturana Blanca h l Malvasía Volcánica h h
Rey l m Moscatel de Grano Gordo Rosa h l Mandón h h
Sabro l m Prieto Picudo h l Mansés de Tibbus h h
Torrontés l m Puerto Alto h l Manto Negro h h
Albillo Mayor m l Verdejo de Salamanca h l Mondragón h h
Beba roja m l Xarel.lo h l Morrastel-Bouschet h h
Cagarrizo m l Albariño h m Ondarrabi Beltza h h
Cañorroyo m l Allarén h m Pampolat de Sagunto h h
Chasselas Doré m l Argamusa h m Pampolat Girat h h
Moscatel de Alejandría m l Valenci Tinto h m Parduca h h
Juan García m l Moravia Dulce h m Parellada h h
Pedro Luis m l Caíño Tinto h m Pedrol h h
Pedro Ximénez m l Caíño Bravo h m Perruno h h
Planta Nova m l Callet h m Petit Bouschet h h
Salvador m l Chasselas Rosé h m Quigat h h
Zalema m l Cherta h m Sabaté h h
Airén m m Ferrón h m Santa Magdalena h h
Alarije m m Folle Blanc h m Señá h h
Albillo de Albacete m m Fumat h m Trobat h h
Albillo Real de Granada m m Giró h m Verués de Huarte h h
Borba m m Jaén Rosado h m Viñaté h h
Chenín Blanc m m Jaén tinto h m
Chapter 5
- 81 -
All plants were grafted onto 41B and were almost 30 years old. The plantation compass was
2.5 m × 2.5 m. Repetitions of the cultivars were randomly arranged in the same plot. Therefore, all
the cultivars were subjected to the same edapho-climatic conditions and traditional management
practices. They were cultivated in dry land, with a training vessel and had no phytosanitary
treatment during the period of study. Mazuela (synonyms Cariñena, Carignan Noir) was used as
the susceptibility control and it was regularly distributed in twelve different locations along the plot
to control the uniformity of the infection.
Methods
Climatic data were recorded for the years of the study. Evaluation of natural infection was
performed from June to September (about 3 weeks after onset of flowering for leaves and before
vintage for bunches). Infection levels were visually estimated following the descriptors OIV-455
and OIV-456 of the International Organization of Vine and Wine (OIV 2007), which refer to the
degree of resistance on leaves and bunches, respectively, using a 1 to 9 scale (1=very low
resistance, 9=high resistance). The cultivars were classified into three classes, depending on the
level of these descriptors, as follows: levels 1–3, a low or very low degree of resistance (high
susceptibility); level 5, a medium degree of resistance (medium susceptibility); and levels 7–9, a
high to very high degree of resistance (low susceptibility).
The following morphological characters of leaves and bunches were selected from the OIV
descriptors list (OIV 2007) to identify factors associated with resistance to powdery mildew: (I)
Young leaf: OIV-051 and OIV-053, (II) Mature leaf: OIV-065, OIV-072, OIV-075, OIV-084 and OIV-
087, (III) Bunch: OIV-202, OIV-203, OIV-204 and OIV-208, and (IV) Berry: OIV-220, OIV-221 and
OIV-223. Spearman Rho coefficients for ranked data were calculated to detect all possible
correlations between morphological and disease variables. All statistical analyses were performed
with the statistical program SPSS v.15.
Results and discussion
For all of the years, the climatic conditions seem to be favourable for the Erysiphe necator
development (Figure 1). Intraspecific variation in the susceptibility to powdery mildew has been
found among the studied Vitis vinifera cultivars as other authors have reported (Doster and
Schanathorst 1985; Li 1993; Eibach 1994). The most frequent level of susceptibility observed was
the medium level (35-50% of the varieties, depending on years), which corresponds to leaves with
attacked patches, usually limited to a diameter of 2 to 5 cm, many attacked berries (up to 30 %),
and most clusters moderately attacked. Between 13% and 52% of the varieties were very
susceptible to powdery mildew on bunches, showing many berries of all clusters attacked and
many cracked berries. On the other hand, between 13% and 36% of the cultivars showed bunches
with low susceptibility. In these cases, only a few berries of all clusters were attacked.
Evaluation of susceptibility to powdery mildew
- 82 -
The maximum degree of resistance for each cultivar and year was calculated, and the modal
data were obtained for the whole period (Table1). A total of 35 cultivars (22%) were very
susceptible to the disease (very low–low resistance on bunches), while another 83 (52.2%)
showed low susceptibility (high to very high resistance on bunches).
0
5
10
15
20
25
30
35
40
May June July August September
Me
diu
m te
mp
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ture
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º)
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140
To
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mm
)
2006 2007 2008 2009
2006 2007 2008 2009
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40
May June July August September
Maxim
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ture
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)
2006 2007 2008 2009
0
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May June July August September
Rela
tive h
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%)
2006 2007 2008 2009
Total rainfall
Med. temperature
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700
May June July August September
So
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2006 2007 2008 2009
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May June July August September
Me
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To
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)
2006 2007 2008 2009
2006 2007 2008 2009
0
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10
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May June July August September
Maxim
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)
2006 2007 2008 2009
0
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May June July August September
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2006 2007 2008 2009
Total rainfall
Med. temperature
0
100
200
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May June July August September
So
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/m2)
2006 2007 2008 2009
Figure 1. Climatic data during the period of study (2006-2009)
Chapter 5
- 83 -
The variety used as the susceptibility control, Mazuela, has proved to be very susceptible to
powdery mildew, as other authors have found (Doster and Schnathorst 1985; Li 1993; Péros et al.
2006). Li (1993) and Péros et al. (2006) also noted a high susceptibility of Cabernet-Sauvignon,
close to the response of Mazuela and the same results were obtained in this study. On the other
hand, Grenache (synonym Garnacha Tinta) has been reported to be less susceptible than
Cabernet-Sauvignon (Boubals 1961; Li 1993); however, our results corroborated this finding only
in the case of susceptibility on leaves because both cultivars showed the same results for
bunches. Péros et al. (2006) found likewise that Grenache was not always distinguished from very
susceptible cultivars in laboratory tests. Results for Folle Blanc (syn. Folle Blanche), Monastrell
(syn. Mourvèdre), Macabeo (syn. Maccabeu), and Garnacha Tintorera (syn. Alicante Bouschet)
have been also reported by Li (1993). We agree with his findings that Folle Blanc and Monastrell
showed a medium level of susceptibility with respect to Mazuela, which was lower in the case of
Garnacha Tintorera. In contrast to values obtained by Li (1993), we observed a minor level of
susceptibility for Macabeo relative to the control, which could be due to the existence of diverse
homonyms for Macabeo.
Results showed that the degree of resistance to powdery mildew on leaves correlates positively
with resistance on bunches. This correlation was high in 2008 (r=0.75), when the degree of
infection was also higher, probably because of the most favourable climatic conditions having
occurred in that year. The coefficient of determination indicated that 56% of the variation in the
berry resistance could be explained by variation in the leaf resistance in that year. However, this
correlation was smaller in 2007 (r=0.52) and 2009 (r=0.38), verifying that the relationship between
variables is not sufficiently consistent in time and may depends on the fungal infection pressure.
Previous studies have demonstrated a relationship between morphological features and the
susceptibility of Vitis vinifera cultivars to powdery mildew. In our study, only two out of the fourteen
ampelographic characters studied were significantly correlated with resistance to powdery mildew.
We detected a significant negative correlation between the degree of resistance on leaves and the
goffering of blades (r=-0.16 to -0.22) and between bunch density and the degree of resistance on
berries (r=-0.17). These two effects could be related with a less ventilation of these types of leaves
and bunches altering microclimatic conditions to favour fungal development. However, the low
correlations obtained do not show a strong relationship. Therefore, the selection of these
morphological characters will not assure resistance in the selected cultivars.
Evaluation of susceptibility to powdery mildew
- 84 -
Conclusions
Knowledge about the degree of susceptibility of each variety makes the selection of less
susceptible cultivars possible for grape growers. This is an important advantage, especially in
areas where climatic conditions are often favourable for the disease, such as the Mediterranean
region. Thus, fungicide treatments may be substantially reduced in an important number of
cultivars, enabling a reduction in economic and environmental costs.
Acknowledgements
This work was funded by INIA (Ministry of Education and Science, Spain), FEDER (European
Union), and D.G. Agriculture (European Commission) through the projects RTA06-120 and
GRAPEGEN06-870.
References
Boubals D. (1961) Etude des causes de la résistance des Vitacées àl`Oidium de la vigne -
Uncinula necator (Schw.) Burr.- et de leur mode de transmission héréditaire. Annales de
l'amélioration des plantes 11: 401-500.
Calonnec A.; Cartolaro P.; Dubourdieu D.; Darriet P. (2004). Effects of Uncinula necator on the
yield and quality of grapes (Vitis vinifera) and wine. Plant Pathol 53: 434-445.
Doster M., Schnathorst W. (1985) Comparative susceptibility of various grapevine cultivars to the
powdery mildew fungus on grapes. Am J Enol Vitic 36: 101-104.
Eibach R. (1994) Investigations about the genetic resources of grapes with regard to resistance
characteristics to powdery mildew (Oidium Tuckeri). Vitis 33: 143-150.
Li H. (1993) Studies on the resistance of grapevine to powdery mildew. Plant Path 42: 792-796.
OIV (Office International de la Vigne et du Vin) (2007) OIV descriptor list for grape varieties and
Vitis species, 2nd edition. Paris, France: OIV website.
http://news.reseau-concept.net/images/oiv_es/client/Code_descripteurs_2ed_ES.pdf.
Accessed 14 July 2008
Péros J.P., Nguyen T.H., Troulet C., Michel-Romitti C., Nnotteghem J.L. (2006) Assessment of
powdery mildew resistance of grape and Erysiphe necator pathogenicity using laboratory
assay. Vitis 45: 29-36.
Pool R.M., Pearson R.C., Welser M.J., Lakso A.N., Seem R.C. (1984) Influence of powdery
mildew on yield and growth of Rosette grapevines. Plant Dis 68: 590-593.
Savocchia S., Stummer B.E., Wicks T.J., Van Heewijck R., Scorr E.S. (2004) Reduced sensitivity
of Uncinula necator to sterol demethylation inhibiting fungicides in southern Australian
vineyards. Australas Plant Path 33: 465-473.
Staudt G. (1997) Evaluation of resistance to grapevine powdery mildew (Uncinula necator [Schw.]
Burr., anamorph Oidium tuckery Berk.) in accessions of Vitis species. Vitis 36: 151-154.
Chapter 6
A tear full of aroma
Aromatic characterization and oenological potential of 21 minor varieties (Vitis vinifera L.)
Sonia García Muñoz, Andriani Asproudi, Félix Cabello, Daniela Borsa
Submitted to European Food Research and Technology (2011)
Chapter 6
- 85 -
Abstract
The homogenization of international wine market led to a gradual impoverishment of the genetic
pool. In fact, in several Spanish Quality Demarcations, the most frequently international varieties
are replacing the local ones. As a result, minor varieties, perfectly adapted to the local
environmental conditions, are nowadays at risk of extinction. The study of minor varieties could
provide useful inputs to satisfy the demand for new and interesting wine products. This work aims
at filling a gap in the existing literature, focusing on the aromatic potential of minor varieties. Here,
the study of glycosidic volatile compounds and the evaluation of the influence of several variables
on aroma composition were considered. Fifty-one glycosidic compounds were identified and
quantified. In order to identify the most powerful aroma compounds of the studies grapevines, the
Odour Activity Values (OAVs) was used. Floral, spicy and phenolic were the most important
odorant series of OAV evaluation. The results revealed differences for glycosidic compounds
according to cultivars, berry colour, clone and sample origin. Moreover the synthesis of some
compounds involved in these differentiations seems to have a genetic component. The
characterization of aromatic potential of several minor varieties, achieved for the first time,
revealed that some of those (Argamusa, Gorgollassa and Pampolat girat) could represent an
excellent option for winemaking and commercial offer diversification strategies, besides being
important for these cultivars conservation.
Key words: Aroma, glycosides, gas-chromatography, flavour
Introduction
The phenomenon of replacement of local grape varieties with widely-spread international cultivars,
such as Cabernet-Sauvignon or Chardonnay, is coming to a standstill. In addition, the wine
consumers taste and preferences have changed during the last few years, since there are other
values and motivations out of aroma and taste for drinking wines, e.g. as marketing attributes and
new wine style (Lesschaeve 2007). Several Quality Demarcations are starting to promote varieties
linked to specific locations, which produce original and high quality wines. Minor varieties,
perfectly adapted to the local environmental conditions, may represent a good option. Even
though they may not be allowed in any Quality Demarcation; their reintroduction in the wine
industry would not only entail an increase in the wine offer, but also provide an important tool for
their conservation. Being the wine aroma one of the most important attributes for influencing
consumer´s preferences, a more detailed study about the aromatic potential of these traditional
varieties appears to be an important first step.
The qualitative and quantitative volatile compounds in grapes are important as a source of
flavour in wines (Fang and Qian 2006). The grape and wine aroma, and specifically its intensity,
has long been one of the principal attributes taken into account for grapevine selection (Duchêne
Aromatic characterization and oenological potential
- 86 -
et al. 2009b). The wine aroma compounds depend on the cultivar (Ferreira et al. 2000; Esti and
Tamborra 2006), on environmental conditions (Ribéreau-Gayon et al. 2006) as well as on
oenological processes such as maceration (Peinado et al. 2004), yeast and fermentation (Loscos
et al. 2007; Ugliano and Moio 2008).
The aromatic compounds have been studied in several grape varieties (Cabrita et al. 2006), as
well as the relationship between the grape variety and the wine obtained (Mulet et al. 1992;
Ugliano and Moio 2008). In grapes those compounds can be found in their free and bound form,
especially as glycosides (Ugliano and Moio 2008). The majority of wines are made with neutral
varieties, characterized by very low, and sometimes not detectable, values of the free volatile
forms content (Cabrita et al. 2006). Consequently, the contribution of those compounds to the final
wine aroma is often negligible (Ugliano and Moio 2008). By contrast, bound compounds, together
with fermentation volatile ones, can contribute to the wine aroma (Ugliano and Moio 2008), since
they can be hydrolyzed by yeast or exogenous enzymes during winemaking (Ugliano and Moio
2008; Loscos et al. 2009) and aging (Fang and Qian 2006).
Although all volatile compounds contribute to the odour (Ryan et al. 2008), only a few of them
play a role in determining varietals aroma (Rocha et al. 2004). However, the compounds with
higher odour activity value (OAV) could define the main odorant notes in musts and wines (Guth
1997; Franco et al. 2004). The selection of these compounds helps understanding their impact on
the aroma profile (Lorrain et al. 2006). This research focuses on the glycosidic volatiles, whose
quantification could define the potential wine aromatic profile, according to the reviewed literature
(Loscos et al. 2009).
Previous studies on the use of aroma compounds in wine characterization have been carried
out for Callet, Manto Negro and Fogoneu, varieties included in several Quality Demarcations of
the Balearic Islands, Spain (Mulet et al. 1992; Forcen et al. 1993). For the remaining ancient
cultivars, the contribution of grape aroma precursor to wine aroma profile has not yet been
studied. In this work we analyzed the aromatic potential of 21 varieties, mainly native from the
Balearic Islands, Spain. The aims of this study were (i) to characterize the glycosidic compound in
grapevine (Vitis vinifera L.), (ii) to approach the aroma profiles of obtained wines considering the
glycosides compounds with major odour activity value, and (iii) to evaluate how different variables
such as the berry colour, the accession (or clone), and the origin of the samples can influence the
aroma profile. Furthermore, improving the knowledge of minor varieties could represent an
interesting option for wine marketing strategies, aiming at satisfying the evolving consumer´s
demand.
Chapter 6
- 87 -
Material and Methods
Plant material and field conditions
We analyzed 32 accessions of Vitis vinifera L. corresponding to 21 different varieties of Vitis
vinifera L. since some accessions or clones of the same variety were studied (García-Muñoz et al.
2011). Some of the studied varieties are allowed in several Spanish Quality Demarcation (Table
1). The varieties are preserved at the Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de
Henares, Spain) and were collected mainly in the Balearic Islands (Spain) but also from the
Levante area and Girona (Table 1).
Table 1. List of the accessions analyzed; conserved at the Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain). Acc.: accession; Id.: code used in the analysis; Origin: PM Palma de Mallorca; GI Girona, LE Levante area; N: Number of Spanish Quality Demarcation where the variety is allowed; - Minor varieties non included in a Quality Demarcation; * allowed out of the Balearic Islands
Main name Acc. Id. Berry colour Origin N
Argamusa E34 ARG.E34 white PM - Batista E23 BAT.E23 red PM - Beba E39 BEB.E39 white GI 1* Beba O16 BEB.O16 white LE 1* Beba O17 BEB.O17 white LE 1* Beba O44 BEB.O44 white LE 1* Beba E14 BEB.E14 white PM 1* Beba E32 BEB.E32 white PM 1* Beba E43 BEB.E43 white PM 1* Beba roja E19 BER.E19 rose PM - Bobal E11 BOB.E11 red PM 6* Bobal E21 BOB.E21 red PM 6* Callet E26 CAL.E26 red PM 2 Eperó de Gall E37 EPE.E37 red PM - Escursach E27 EXC.E27 red PM - Fernandella E22 FER.E22 red PM - Fogoneu E24 FOG.E24 red PM 1 Fogoneu E16 FOG.E16 red PM 1 Giró E31 GIR.E31 red PM - Giró E36 GIR.E36 red PM - Gorgollassa E25 GOR.E25 red PM - Mansés de Tibbus E30 MES.E30 red PM - Manto Negro E29 MTN.E29 red PM 2 Manto Negro E20 MTN.E20 red PM 2 Mandón E15 GAL.E15 red PM - Pampolat girat H24 PAM.H24 red LE - Pensal Blanca SCL PEN.SCL white PM 2 Quigat E38 QUI.E38 white PM - Quigat E33 QUI.E33 white PM - Sabaté E35 SAB.E35 red PM - Valenci Tinto E18 BEN.E18 red PM - Viñaté E45 VIN.E45 white PM -
Aromatic characterization and oenological potential
- 88 -
The grapes were picked by hand, in excellent sanitary conditions, at commercial maturation
during 2007 vintage. Approximately 1.5 kg of grapes were collected from the whole vine, ensuring
the selection of a fully representative sample. Three sub-samples of 100 berries were randomly
selected from the grape sample from each accession. They were stored in labelled glass
containers at -30 ºC until analysis were performed.
Plants were almost 20 years old and were grown under the same field conditions. All of them
were unirrigated and grafted onto 41B, trained vessel and eight buds per vine were left in winter
pruning. The distance between vines was 2.5 m and the one within rows was 2.5 m.
Sample preparation
Aroma compounds were extracted following a previously described method (Cabrita el al. 2006)
with some modifications. The samples of 100 berries were frozen until the analysis were carried
out. The seeds were discarded before defreezing the samples at a temperature of 4 ºC.
Subsequently each sample was homogenized in an Omni-Mixer (Sorvall, Labequip, Ontario,
Canada) where 100 mg Na2S2O5 were added, centrifuged for 15 min at 4000 rpm and the liquid
phase was recovered. The pellet was washed with pH=3.2 solution and centrifuged again. This
operation was repeated twice. The liquid phases were then assembled up to 300 mL, adding 100
mg of a commercial pectolitic enzyme (Vinozym, Novo Nordisk Ferment Ltd, Dittingen) without
glycosidase activity. After a period of six hours they were filtered using filtered discs (Whatman
589). 150 mL of these extracts were passed through a Sep Pack Cartridge C18 5 g (Waters,
Milford, MA, USA) previously activated with 20 mL of methanol (Sigma–Aldrich Co, St Louis, MO,
USA) and 50 mL of water. After treating each sample, the cartridge was washed with 50 mL of
water and subsequently with 30 mL of dichloromethane. The bound compounds were eluted with
25 mL of methanol. Methanol was then eliminated under vacuum and the residue was solubilised
in 5 mL of 0.1 M citrate-phosphate buffer (pH=5.0; 51.5% 0.2 M sodium phosphate and 48.5% 0.1
M citric acid). When analyzing red variety samples, 1 g of polyvinylpolypyrrolidone (PVPP; Sigma,
St Louis, MO, USA) was added to remove the phenolic compounds. The glycosidically-bound
fraction was hydrolyzed with 200 µL of a commercial glycosidase rich enzyme (Cytolase M102,
Ferrari- s.r.l. Italy) at 40 ºC for 24 h. A known concentration of internal standard (1-heptanol at a
final concentration of 100 µg L-1; Fluka, Sigma–Aldrich Co.; St Louis, MO, USA; purity
assay>99.0%) was added to the mixture containing the aglycons released by enzymatic hydrolysis
at room temperature. The suspension was then centrifuged at 4000 rpm during 15 minutes to
remove PVPP. The supernatant was then passed through a Sep Pack Cartridge C18 1 g
previously activated with 5 mL of methanol and 10 mL of water. The free compounds were
recovered with 12 mL of dichloromethane. After drying with anhydrous sodium sulphate, the
obtained volume was evaporated at ambient temperature and pressure. After concentration the
sample was ready for GC/GC-MS analysis.
Chapter 6
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Gas Chromatography-Mass Spectrometry conditions
The separation was achieved using an Innowax 19091-N (Agilent-J&W Scientific, Santa Clara,
CA, USA) capillary column (30 m x 0.25 mm ID, 0.25 μm d.f.), using helium as the carrier gas at
70 kPa. The gas chromatograph was a Hewlett Packard 5890 series II connected to a selective
detector MSD 5970 (Hewlett Packard, Wilmington, DE, USA). The injector temperature was 250
ºC and interface temperature was 230 ºC. The oven temperature program was set at 45 ºC for 2
min, then linearly increased, at a rate of 30 ºC min-1 to 60 ºC, 2 ºC min-1 to 160 ºC, and 3 ºC min-1
to 230 ºC. The final temperature was maintained for 13 min. The injection mode was splitless for 2
min. Acquisition mass ranged was from 28 to 300 u.m.a. and ionization energy was 70 eV. Volatile
compounds were identified by comparing the retention time and mass spectra with those reported
in the literature (di Stefano 1991; Cabrita et al. 2006). All mass spectra were also compared with
those of the data system libraries (Wiley275 and libraries of mass spectra). Semi-quantitative data
were obtained by the ratio of area of individual compounds versus internal standard area (1-
heptanol).
Threshold determination and odour activity value (OAV)
The odour perception threshold is defined as the lowest concentration able to produce a
sensation. In order to identify the most powerful aroma compounds of the studied grapevines and
the most interesting varieties, the odour activity value (OAV) was used. This was calculated as the
ratio between the concentration value of the compound and the perception threshold found in the
literature (Table 2). Previous studies report that when the OAV is greater than 0.20 the
compounds contribute to the aroma (Versini et al. 1994). Consistently with the literature, the
compounds with OAV>0.20 were grouped in seven odorant series (floral, spicy, citric, fruity,
phenolic, roasted and sweet; Table 2). The OAV of a series was calculated by summing up the
individual OAV for all the compounds included in each odorant series. To allow comparisons of the
aroma profiles across different grapevines the relative aroma values in each odorant series was
calculated.
Aromatic characterization and oenological potential
- 90 -
Table 2. Odour threshold (µg L-1
), odour description and odorant series (1, citric; 2, floral; 3, fruity; 4,
phenolic; 5, roasted; 6, spicy; 7, sweet). Only the compounds with OAV>20% are shown
Aroma compound
Odour threshold
(µg L-1
) Odour description Odorant series
Benzaldehyde 300 (Darriet et al. 2002)
Almond, fragrant (Peinado et al. 2004), piney, fruity (Noble et al. 1980), roasted (Franco et al. 2004)
2, 5 (Franco et al. 2004)
Benzyl alcohol 620
(Latrasse 1991)
Fruity (Lorrain et al. 2006), floral (Fang and Qian 2006; Lorrain et al. 2006), roasted, toasted (Franco et al. 2004)
5 (Peinado et al. 2004)
2-phenylethanol 1500
(Zamuz et al. 2006)
Floral, fruity, grassy (Noble et al. 1980), rose, honey (Franco et al. 2004)
2 (Zamuz et al. 2006)
Eugenol 5 (Guth 1997)
Clove, balsamic, peper (Arroyo et al. 2009), cinnamon, wood (Moyano et al. 2002)
4, 6 (Moyano et al. 2002)
4-vinylguaicol 40 (Guth
1997) Spicy, woody (Ugliano and Moio 2008), phenolic (Arroyo et al. 2009)
4 (Arroyo et al. 2009)
4-vinylphenol 180
(Boidron et al. 1988)
Spicy, phenolic, cypress, vanilla (Lorrain et al. 2006)
6 (Lorrain et al. 2006)
Linalool 15 (Guth
1997) Citrus, floral, sweet, grape-like (Peinado et al. 2004)
2, 3, 7 (Peinado et al. 2004)
α-terpineol 80 (Zamuz et al. 2006)
Floral, lilac, sweet (Peinado et al. 2004) 2, 7 (Peinado et al.
2004)
Citronellol
18 (Ribéreau-
Gayon et al. 2006)
Citronella (Ribéreau-Gayon et al. 2006), green, clove (Ferreira et al. 2001)
1, 2 (Zamuz et al. 2006)
Nerol 80 (Zamuz et al. 2006)
Floral (Ugliano and Moio 2008; Moyano et al. 2002), green (Moyano et al. 2002)
2 (Moyano et al. 2002)
Geraniol 30 (Guth
1997) Rose (Ribéreau-Gayon et al. 2006), floral (Ugliano and Moio 2008)
2 (Ugliano and Moio 2008)
Statistical analysis
The aromatic compounds and varieties were analyzed using Stepwise Forward Discriminant
Analysis (SFDA) and Principal Component Analysis (PCA). SFDA was used in order to
differentiate white, rose and black varieties, and the origin of Beba samples. The PCA analysis
allowed establishing a relationship between the different aroma compounds and the studied
grapevines, as well as identifying the most important compounds in white, rose and red varieties.
The correlations between different accessions of the same variety were evaluated using Pearson
correlation analysis (r). The differences between OAV for each series and between varieties
included and excluded from Quality Demarcations were checked by ANOVA followed by Tuckey´s
HSD test to enable pairwise comparisons of means (p<0.05). All statistical analyses were
performed using XLSTAT 2009 version.
Chapter 6
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Results
In this study, 51 glycosidic compounds from 21 wine varieties were identified and classified into
four categories (alcohols, benzenes, terpenes and norisoprenoids; Appendix III: Tables 1, 2;
Appendix III: Figures 1, 2). Total amount of glycosidic compounds varied from 947 (Fogoneu
variety) to 2911 µg kg-1 (Sabaté variety). The compounds benzyl alcohol and 2-phenylethanol
were the most abundant compounds for all the considered varieties. At the same time, all varieties
presented high values of benzenic compounds, except Beba (E43 and O44 accessions), Beba
roja and Giró (E31 accession) which instead presented high value of norisoprenoid compounds
and Giró (E36 accession) which presented high value of terpenic compounds.
Glycosidic compounds characterization
Two PCA were done, one for each grape colour (one for red and other one for white and rose
varieties). The first PCA axis for the red varieties (Figure 1a) explained the 30.2% of the total
variance. The most important compounds in the first axis were hydroxy geraniol and diol (2,6-
dimethyl-3,7-octadien-2,6-diol), accounting for 6.9% and 5.9%.of the total variance explained by
this axis respectively. These alcohols were highly related with Batista, Pampolat girat and Giró
varieties. The second axis explained 19.1% of the total variance and it was related with benzyl
alcohol (7.3% of the variance), trans-2-hexenol (7.2%) and benzaldehyde (6.5%). Sabaté was
related with benzyl alcohol compound showing a more than double value with respect to the rest
of the analyzed varieties. Pampolat girat, Batista, Gorgollassa and Fernandella varieties were
correlated to norisoprenoid and terpenic compounds. In addition, Pampolat girat was correlated to
3-oxo-α-ionol, displaying the highest concentration (234 µg kg-1). Fogoneu, Callet, Manto Negro,
Eperó de Gall, Bobal (accesion E21) and Mansés de Tibbus varieties obtained negative values on
both axes. Giró was correlated to linalool and cis-8-hydroxy-linalool compounds, reaching the
highest values (60 and more than 240 µg kg-1 respectively). The Cresol aroma exerted an
influence on Bobal (E21 accession), Callet, Fogoneu and Galmete classification. Finally, Sabaté,
Bobal (E21 accession), Beba negra, Excursach and Galmete showed a correlation with benzenic
compounds.
As regards the differences among white and rose varieties (Figure 1b), the first PCA axis
explained 40.8% of the total variation and was associated with diol (2,6-dimethyl-3,7-octadien-2,6-
diol), explaining 4.5% of the variance, 4-vinylguaicol (4.3%), hexanol (4.2%) and blumenol C
(4.2%). The second axis explained 23.5% of the variance being mainly related with 3-oxo-α-ionol
(6.9%), hydroxy citronellol (6.2%) that separated Argamusa, Pensal Blanca and Quigat varieties
and cis-8-hydroxy-linalool (5.4%). All the Beba accessions were linked with norisoprenoid and
terpenic compounds, especially with 3-oxo-α-ionol, which attains the highest value (>166 µg kg-1)
and with cis-8-hydroxy-linalool (>60 µg kg-1). Quigat was particularly related to methyl syringate,
this being the only white variety presenting this compound. Argamusa was related with trans-2-
Aromatic characterization and oenological potential
- 92 -
hexenol, dihydroconiferyl alcohol and endiol. Viñaté showed strong relation with benzaldehyde
(140 µg kg-1).
a)
BAT.E23
BEN.E18
BOB.E11
BOB.E21
CAL.E26
EPE.E37
EXC.E27FER.E22
FOG.E16
FOG.E24
GAL.E15
GIR.E31 GIR.E36
GOR.E25
MES.E30
MTN.E20
MTN.E29
PAM.H24
SAB.E35
1
2
3
33
34
5
35
36
6
38
40
41
89
42
44
1011
48
49
50
21
2223
51
24
25
1314
15
26
31
16 1718 32
-15
-10
-5
0
5
10
15
-20 -15 -10 -5 0 5 10 15 20 25
F2 (19.1
0 %
)
F1 (30.16 %)
Biplot (axes F1 and F2: 49.26 %)
b)
ARG.E34
BEB.E14
BEB.E32
BEB.E39
BEB.E43
BEB.O16BEB.O17
BEB.O44
BER.E19
PEN.SC
QUI.E33
QUI.E38
VIN.E45
1
2
3
33
345
3536
3738
40
7
41
8
9
42
43
44
10
11
45
4647
49
50
12
21
22
23
24
25
13
15
26
27
28
29
30
31
16
17
18
19
20
-30
-20
-10
0
10
20
30
-50 -40 -30 -20 -10 0 10 20 30
F2 (23.5
5 %
)
F1 (40.84 %)
Biplot (axes F1 and F2: 64.39 %)
Figure 1. Principal Component Analysis plots for PCA1 and PCA2 of the aroma volatile compounds used for (a) red and (b) rose and white grapevines. Symbols: black circles (grapevine varieties) and grey circles (aroma from glycosidic precursors; keys in Appendix III: Tables 1, 2). The averages of the three replicate samples were used for the analysis
Chapter 6
- 93 -
Odour active value (OAV)
Six benzenic compounds (benzaldehyde, benzyl alcohol, 2-phenylethanol, eugenol, 4-vinylguaicol,
4-vinylphenol) and five terpenic compounds (linalool, α-terpineol, citronellol, nerol, geraniol)
displayed an OAV higher than 0.20 (Appendix III: Tables 3, 4). All the odorant series showed
statistically significant differences (p<0.001) in both red and white varieties. Floral, spicy and
phenolic were the most important odorant series in all the studied cultivars (Figures 2, 3).
0
5
10
15f loral
spicy
citric
f ruityphenolic
roasted
sweet
BAT.E23 MES.E30 SAB.E35
0
5
10
15f loral
spicy
citric
f ruityphenolic
roasted
sweet
EXC.E27 MTN.E20 MTN.E29 BEN.E18
0
5
10
15
20f loral
spicy
citric
f ruityphenolic
roasted
sweet
FOG.E16 FOG.E24 GOR.E25
0
5
10
15f loral
spicy
citric
f ruityphenolic
roasted
sweet
BOB.E11 BOB.E21 PAM.H24
0
20
40
60f loral
spicy
citric
f ruityphenolic
roasted
sweet
FER.E22 GIR.E31 GIR.E36
0
5
10
15f loral
spicy
citric
f ruityphenolic
roasted
sweet
CAL.E26 EPE.E37 GAL.E15
Fig. 2 Mean Odour Activity Values (OAV), in relative unities, for odorant series in red varieties. All the
series between cultivars were significantly different (p < 0.05). Keys in Table 1
Figure 2. Mean Odour Activity Values (OAV), in relative unities, for odorant series in red varieties. All the series between cultivars were significantly different (p <0.05). Keys in Table 1
Aromatic characterization and oenological potential
- 94 -
0
5
10
15
20
25f loral
spicy
citric
f ruityphenolic
roasted
sweet
BEB.E39 BEB.E43 PEN.SC
0
5
10
15
20
25f loral
spicy
citric
f ruityphenolic
roasted
sweet
BEB.O16 BEB.O17 BER.E19
0
5
10
15
20
25f loral
spicy
citric
f ruityphenolic
roasted
sweet
ARG.E34 QUI.E33 QUI.E38 VIN.E45
0
10
20
30
40
50f loral
spicy
citric
f ruityphenolic
roasted
sweet
BEB.E14 BEB.E32 BEB.O44
Figure3. Mean Odour Activity Values (OAV), in relative unities, for odorant series in white varieties. All the series between cultivars were significantly different (p<0.05). Keys in Table 1
Among red varieties, Giró (E36 accession) was the most important in floral, fruity and sweet
series. Batista was the spiciest variety and, together with Gorgollassa, the most phenolic one.
Sabaté scored the highest in the roasted series. Among rose and white varieties, Beba (E14
accession) presented the highest OAV in the floral, fruity and sweet series, and Viñaté was the
most important variety in spicy, phenolic and roasted series.
In the cases where several accessions of the same variety were studied, only Beba and Giró
varieties displayed statistically significant differences (p<0.001). As a matter of fact, three
accession of Beba variety presented the highest OAV, E14 in floral, citric and sweet series; O44 in
citric series and E32 in roasted series. As a far as Giró is concerned (E36 accession) presented
the highest OAV in floral, fruity and sweet series, while E31 accession presented the highest OAV
in citric series.
A comparison between varieties included in a Demarcation of Quality and those excluded
highlighted statistically differences in the OAV of red varieties, in the floral and phenolic series
(p<0.001). In both cases, the varieties not included in a Quality Demarcation were the richest
ones. White varieties presented statistically significant differences (p<0.001) in several series. In
this case, varieties included in a Quality Demarcation turned out to be the richest ones in the fruity
and sweet series. However, the varieties not belonging to any Quality Demarcation presented the
highest values in the spicy, phenolic and roasted series.
Chapter 6
- 95 -
Role of the berry colour
The within-class covariance matrices reported differences between berry colour (SFDA analysis,
p<0.001). All the studied samples were classified correctly except one replicate of Beba (O16
accession), which changed from the white to the rose category. The percentage of correct
classification for the 96 samples analyzed was 98.96% (Figure 4). The most suitable variables for
this classification were two alcohols, three benzenic compounds and four terpenes. As regards the
alcohols, hexanol turned out to be higher in red varieties (p<0.001; 65.55 vs 33.93, 36.76 µg kg-1
per red, rose and white samples respectively) and tirosol was present exclusively in red varieties
(p=0.026; 4.42 µg kg-1). As regards the benzenic compounds, 2-phenylethanol was higher in rose
samples (p<0.001; 257.77 vs 186.80, 238.97 µg kg-1 per rose, red and white samples
respectively), 4-vinylphenol was higher in red samples (p<0.001; 91.36 vs 10.14 and 14.25 µg kg-1
per red, rose and white samples respectively) and methyl vanillate appeared to be higher in red
samples than in white samples (p<0.001; 22.20 vs 3.35 µg kg-1 respectively). Finally, the four
relevant terpenes were trans-furan linalool oxide, higher in rose samples (p=0.033; 16.21 vs 8.35
and 7.05 µg kg-1 per rose, red and white samples respectively), α-terpineol was higher in red
followed by rose and white varieties (p=0.016; 11.06, 7.21 and 4.76 µg kg-1 respectively), endiol,
higher in white samples than in red varieties (p=0.002; 2.32 µg kg-1 vs 0.74 µg kg-1 respectively),
and p-menthene-7,8-diol, higher in red samples (p=0.026; 28.12 vs 16.78, 11.64 µg kg-1 per red,
rose and white samples respectively).
Red
Rose
White
-6
-4
-2
0
2
4
6
8
-8 -6 -4 -2 0 2 4 6 8
F2 (6.8
0 %
)
F1 (93.20 %)
Observations (axes F1 and F2: 100.00 %)
Red Rose White Centroids
Figure 4. Discriminant Analysis (SFDA) of aroma from glycosidic precursors from grapevines (Vitis vinifera L.) grouping according grape colour. Symbols: triangles (centroids of each group) and circles (grapevine varieties). Three replicates per sample were used in the analysis
Aromatic characterization and oenological potential
- 96 -
Role of the clone
The correlation between accessions of the same variety was higher than 0.90, with the notable
exception of Beba variety (r=0.81). All accessions of Bobal, Giró, Manto Negro and Quigat
varieties presented r>0.97. Nonetheless, they showed slight differences between accessions of
the same variety (Appendix III: Tables 1, 2).
Role of the origin of the Beba accessions
If we consider the origin of each Beba accession (PM, GI and LE), the result of the SFDA method
indicated that the within-class covariance matrices were different (p<0.001). All the varieties were
classified correctly (Figure 5). The variables that showed significant differences were one
benzenic compound, namely homovanillyl alcohol, higher in samples from PM (p=0.002; 26.22 vs
10.57, 13.82 µg kg-1; PM, GI, LE, respectively) and four terpenes, namely α-terpineol, higher in
samples from PM (p=0.036; 8.46 vs 4.53, 5.93 µg kg-1; PM, GI, LE respectively), trans-pyran
linalool oxide, higher in PM samples (p=0.004; 22.03 vs 14.68, 15.89 µg kg-1; PM, LE, GI,
respectively), citronellol, exclusively present in samples from LE (p=0.078; 1.92 µg kg-1) and diol
(2,6-dimethyl-3,7-octadien-2,6-diol), lower in samples from PM (p=0.011; 7.55 vs 9.72, 10.71 µg
kg-1; PM. LE, GI, respectively).
GI
LE
PM
-5
0
5
10
15
-10 -5 0 5 10 15
F2 (12.0
6 %
)
F1 (87.94 %)
Observations (axes F1 and F2: 100.00 %)
PM LE GI Centroids
Figure 5. Discriminant Analysis (SFDA) of aroma from glycosidic precursors from Beba accessions grouping according geographical origin: Palma de Mallorca (PM), Levante area (LE) and Girona (GI) Symbols: triangles (centroids of each group) and circles (grapevine varieties). Three replicates per sample were used in the analysis
Chapter 6
- 97 -
Discussion
Glycosidic compounds characterization
The results achieved in this study for Callet and Manto Negro were consistent with previous
descriptions, even though different techniques have been used and the varieties were planted in a
different location (Forcen et al. 1993). Some aromatic compounds, such as benzyl alcohol,
benzaldehyde (Rosillo et al. 1999; Bueno et al. 2003), 2-phenylethanol (Bueno et al. 2003;
Genovés et al. 2005), or 1-hexanol (Genovés et al. 2005) have been found to be a key factor in
the characterization of grapevines. All of these compounds were important glycosidics in this
study.
3-oxo-α-ionol was relevant for the classification of both white and red varieties; however other
authors found the same quantity of 3-oxo-α-ionol in other 13 different varieties (Rosillo et al.
1999). The presence of this compound could be related to biological activity (Stuart and Coke
1975), being one of the most important C13-norisoprenoids in grapevine (Baumes et al. 2002).
This is the case for Pampolat girat and Beba varieties. Vinylphenol was always accompanied by
vinylguaicol and it is well-known that these compounds are products from of the breakdown of p-
coumaric and ferulic acid that are present in grapes and musts (Chatonnet et al. 1995).
Several red varieties were characterized by cresol, which presents a high odorant impact
acknowledged by neurological studies (Ryan et al. 2008). On the other hand, methyl syringate, a
shikimate derivative, was present in all red varieties and in only white variety namely Quigat,
although the existence of this compound in other white cultivar has been previously recorded
(Cabaroglu 2002). Benzaldehyde commonly associated with presence of Botritis cinerea (Delfini
1991), was present in Viñaté variety with the highest value (140 µg kg-1), however no other
compound associated with Botritis cinerea, for instance 1-octen-3-ol, has been found (Pallotta et
al. 1998). Benzaldehyde has also been reported with significant values in other Spanish varieties
(Bueno et al. 2003; Genovés et al. 2005), therefore we could consider a high amount of this
compound to be a characteristic of this variety. Blumenol C played an important role in the
characterization of white and rose varieties. This compound is a derivate of Blumenin (Maier et al.
1995), which has been proved to possess antifungal properties in barley and wheat (Fester et al.
1999). As a consequence, this compound could have a function in resistance mechanisms. The
mentioned aroma compounds from glycosidic precursors are important compounds for the
characterization of the studied cultivars.
Odour active value (OAV)
The aroma compounds impact selection is based on three factors: the perception threshold, the
compounds concentration (Lorrain et al. 2006), and the criterion to select the OAV. Most studies
consider the OAV to be higher than one (Guth 1997; Rocha et al. 2004), or 0.20 (Versini et al.
1994). Aromatic compounds presenting an OAV lower than one contribute to the aroma profile in
Aromatic characterization and oenological potential
- 98 -
neutral varieties (Escudero et al. 2004; Loscos et al. 2007), being the key compounds to define
the aroma in a beverage (Ryan et al. 2008). Moreover, the attribution of an odorant series to
volatile compounds is a validated procedure and represents the first step to approach the wine
aroma profile (Ferreira et al. 2000; Franco et al. 2004). This procedure allows interpreting the
outcomes of the chemical analysis via a sensory perception analysis to obtain an aroma
description. The results found in this study corroborate this hypothesis, since the compounds with
higher odour active value have been proved to be odour-active compounds in musts (Franco et al.
2004) and wines (Rocha et al. 2004; Ugliano and Moio 2008).
When several accessions of Beba and Giró varieties were studied, differences among their
aromatic profiles were found. It seems possible to choose clones with higher aroma potential to
obtain wines with different aroma profile (Marais and Rapp 1991; Botelho et al. 2010).
Comparing the aromatic profile series between the varieties included and excluded from Quality
Demarcations, we have demonstrated that excluded varieties had greater profiles in several
odorant series with respect those included in Quality Demarcations. An example of this is the
Gorgollassa variety, whose wines obtained several prizes towards the end of the XIX century
(Anonymous 1878), nowadays this variety is excluded from all Quality Demarcations.
Role of the berry colour
The differences between red and white grapevines are caused mainly by a mutation on two genes
(Walker et al. 2007). However, the origin of most of aroma compounds in plants is unknown,
despite of the last research done in this field among the last years (Dudareva et al. 2006; Chen et
al. 2011). Due to the different aroma composition in grape cultivars the wine made from white and
red varieties can be clearly differentiated, since the aromatic profile depends on the cultivars
(Ferreira et al. 2000; Esti and Tamborra 2006) and the relationship between grape and wines
aroma compounds has been demonstrated (Mulet et al. 1992; Ugliano and Moio 2008).
The influence of the berry colour in the aroma characterization of Spanish varieties has been
also studied by different authors (Muñoz-Organero and Ortiz 1997; Rosillo 1999). The results of
this study are consistent with the reviewed literature, since the varieties were grouped and
differentiated according to the berry colour. However, volatile compounds were different from
those found in other studies (Rosillo et al. 1999), in which the grapevines did not easily fall into the
identified groups (Muñoz-Organero and Ortiz 1997; Rosillo et al. 1999). This could be explained
considering the larger number of grapevines included in this study with respect to other studies.
Our results showed that two alcohols, three benzenic compounds and four terpenes were the
main volatile compounds for the differentiation of white and red varieties. The hexanol compound
reached higher values in red varieties, which is consistent with previous studies (Rosillo et al.
1999). This compound has a grape origin (Ferreira et al. 2000) and it is scarcely affected by the
fermentation process (Bueno et al. 2003; Esti and Tamborra 2006). As regards the differences
Chapter 6
- 99 -
among benzenic compounds between white and rose varieties, it could be suggested a link
between the biosynthetic pathway of benzene compounds and the shikimic acid pathway that
leads to the synthesis of polyphenols, having them a point of contact represented by the
phenylalanine (Haslam 1998; Dudareva et al. 2006). The synthesis of terpenic compounds has
been appointed to be related to a genetic component (Duchêne et al. 2009a; Chen et al. 2011).
Therefore, the genetic differentiation between red and white cultivar might be also related to the
different grape aroma compound found in white and red varieties.
Role of the clone and samples origin
Differences between grapevine clones based on aroma compounds has been appointed by other
authors (Marais and Rapp 1991; Duchêne et al. 2009b; Botelho et al. 2010), as well as the
influences of the geographic origin over the aroma compounds in must (Marais and Rapp 1991;
Mulet et al. 1992; Zamuz and Vilanova 2006). The clones of Beba variety differed as far as their
glycosidic compounds were concerned, despite the fact that all varieties were planted in the same
location, the quality and value of the glycosidic compounds was statistically different depending on
the origin of Beba accessions, which could be Peninsular (Girona and Levante area) or island
origin (Balearic Islands). Several benzenic or terpenic compounds have proved to be a useful in
classifying must geographic origin, for instance α-terpineol, one of the most odoriferous
monoterpene alcohols not affected by the winemaking procedure (Esti and Tamborra 2006), or diol
(2,6-dimethyl-3,7-octadien-2,6-diol) an aroma precursor (Bayone et al. 2003). These terpenic
compounds were crucial for the geographic grouping of Beba accessions. The synthesis of some
terpenic compounds has been appointed to have a genetic component (Duchêne et al. 2009a;
Duchêne et al. 2009b). The Beba variety, an ancient variety recorded in the Balearic Islands since
1730, in Levante area since 1791, or in Girona since 1877 (Anonymous 1878), has been
conserved at the Vitis Germplasm Bank “Finca El Encín” since the beginning of the XX century. As
a result, the Beba variety could have adapted to local environmental conditions, improving the
synthesis of the aroma metabolic route with a cumulative effect over time.
Conclusions
This report is the first study addressing the potential aroma characterization of several Spanish
minor varieties. Differences in the aroma profile between varieties included and excluded from
Quality Demarcations have been assessed in several odorant series. Minor varieties excluded
from Quality Demarcations appear to be more aromatic and, as a consequence, Argamusa,
Gorgollassa and Pampolat girat varieties could be taken into consideration for the development of
new wine market strategies, which could also play an important role in the conservation of these
cultivars.
Glycosidic compounds were crucial to differentiate white, rose and red varieties. Significant
differences between clones of the same variety have been identified as well as differences in
Aromatic characterization and oenological potential
- 100 -
benzenic and terpenic compounds based on the origin of the Beba variety. The results suggest
that cultivar, berry colour, and cultivar adaptations to location and environmental conditions play
an important role in the metabolic pathways to obtain the aromatic compounds. These metabolic
pathways seem to have a genetic component.
However, after this study which has permitted to individualize the more significant compounds
that characterize the potential aroma of those varieties, other criteria might be analysed.
Agronomic characterization and further investigations, such as GC–olfactometric studies or
quantitative data, are needed to verify the impact of the odour-active compounds identified in this
analysis.
Acknowledgements
Financial support from INIA project RTA 04 175-C3-3. Sonia García Muñoz was supported by a
PhD scholarship from INIA. We thank to Josu G. Alday for statistical advice. Sonia García Muñoz
thanks to CRA-Centro di Ricerca per l’Enologia, Asti (Italy) for welcoming her during her visiting
period.
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Chapter 7
We will meet at 11 am
Sensory characterization and factors influencing quality of wines made from 18 minor varieties (Vitis
vinifera L.)
Sonia García Muñoz, Gregorio Muñoz Organero,
Encarnación Fernández-Fernández, Félix Cabello
Submitted to Journal of the Science of Food and Agriculture (2011)
Chapter 7
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Abstract
The oenological market is alive, always eager for new varieties to satisfy the consumer´s demand.
Thus, the minor or autochthonous wine varieties might be strong candidates to fill this gap.
However the potential of most of these wine varieties is unknown. The quality of wine is difficult to
assay, nevertheless quantitative and descriptive analysis are the most used methods in wine
sensory characterization. The aim of this study was to characterize wines made from local
cultivars using sensorial description. Wines including red, white and rose, made from 18 minor
varieties in two vintages were analysed. Analysis of Variance, Principal Component Analyses and
Partial Least Squares were used to track the influence of vintage, the inclusion of variety in
Denomination of Origin (DO) and agronomical parameters over the sensorial attributes scored by
21 expert wine tasters. Expert´s preference map was also performed to identify the oenological
possibilities of these wines.
This study highlights that the used questionnaire and the experts were efficient for the tasted
wines. The effect of vintage was more important than the Denomination of Origin over both
chemical and sensory characterization. Sensorial analysis conducted on wines demonstrated
significant correlation between sensory attributes and agronomical parameters. Vegetative and
productive agronomical parameters influenced inversely over aroma and taste scores by experts.
The management of these parameters would improve the quality of wines. This study also
highlights the wine market possibilities of minor varieties which have been accepted by wine
experts. The varieties located in the best position of the expert preference map should be
considered by technicians due to their quality, sometimes even better mapped than the wines
made from varieties allowed in Spanish DOs. This information would gain a more interest of minor
or autochthonous wine varieties, being the key for their preservation.
Key words: expert preference, local cultivars, sensory analysis, vintage, wine quality
Introduction
Spain is an important viticulture area in the world, which is divided on 73 Designations of Origin
(DO) and included 250 different cultivars in its national grapevine catalogue, being the fourth
country in grapevine diversity in the European Union behind Italy, Portugal and France (Lacombe
et al. 2011). Despite of the high number of autochthonous cultivars the international varieties are
widely spread and their cultivation are allowed in most of the Spanish DO. In contrast, most of the
minor varieties that could be called “autochthonous” are not included in any DO in spite of some of
them (e.g. Gorgollassa) presents a great oenological potential (Chapter 6; Gutierrez Afonso et al.
1998). DOs play an important role in food and wine marketing strategies (Douglas et al. 2001;
Skuras and Vakrou 2002), since they are based not only in the geographic area but also in wines
quality and originality. Nowadays, DOs are looking for wine varieties (Vitis vinifera L) link to the
Sensory characterization and factors influencing quality of wines
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site (“authoctonous”), which could provide original and high quality wines, with the aim to increase
market possibilities (Koussissi et al. 2008; Bertuccioli 2010). The use of minor varieties could be
an excellent option to satisfy DO requirements.
The quality of the wines depends on grape variety, vintage and soil-type (Maitre et al. 2010),
and bedside the quality of wine is difficult to assay (Charters and Pettigrew 2007), quantitative and
descriptive analysis are the most used methods in wine sensory characterization (Murray et al.
2001; Perrin et al. 2007). The descriptive analysis of the commercial wines made with international
grape cultivars have been largely studied as Riesling (Fischer et at 1999; Douglas et al. 2001),
Sauvignon blanc (Parr et al. 2007; Lund et al. 2009), Chardonnay (Lee and Noble 2006),
Cabernet-Sauvignon (Tao et al. 2009) or Pinot noir (Girard et al. 2001); studies related to this topic
are available in Spanish commercial wines (de la Presa-Owens and Noble 1995; Vilanova et al.
2008; Rodríguez-Nogales et al. 2009; Muñoz-González et al. 2011). However, descriptive analysis
of the not commercial wines done with local or autochthonous grape varieties are not been very
usual (Gutierrez Afonso et al. 1998) despite of the great interest that this varieties are arousing
nowadays, since oenology production is a dynamic process of that need to be always adapted to
changes and demands on wine market (Bertuccioli 2010).
The aim of this study was to characterize wines made with local cultivars using sensorial
description. Thus, wines including red, white and rose, made from 18 minor varieties have been
studied in two vintages. The effect of vintage and varieties included or not in DO over the chemical
analysis as well as over the wine sensorial descriptors was analyzed. The correlations between
chemical analysis and agronomical variables and sensory characterization were also considered.
Expert´s preference map was performed to identify the oenological possibilities of these wines
made from minor varieties, providing a valuable tool to make out the potential of this wines in
future wine market. It hopes that this information would lead to improve the quality of the wines
and help to gain a more interest of autochthonous varieties.
Material and Methods
Plant material selection, field conditions and agronomical parameters
The cultivars (Vitis vinifera L.) were selected according to two criteria (1) their geographic origin,
all of them were collected in the Balearic Islands and were linked to the site (García-Muñoz et al.
2011), and (2) varieties allowed or not in Spanish DO were selected (Table 1).
Chapter 7
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Table 1. List of the wines analyzed; conserved at the Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de Henares, Spain). N: Number of Spanish Designation of Origin where the variety is allowed; - Minor varieties non included in a Designation of Origin. All the wines were mono-varietal (from 100 % of the same variety) except Fogoneu and Eperó de Gall wine which was done from (63 and 37% respectively from each variety)
Varieties used Berry colour Winemaking protocol Studied vintage N
Batista Black Red 2006, 2007 - Bobal Black Red 2006, 2007 6 Callet Black Red 2006, 2007 2 Eperó de gall Black Red 2006, 2007 - Excursach Black Red 2006, 2007 - Fogoneu Black Red 2006, 2007 1 Mandón Black Red 2007 - Gorgollassa Black Red 2006, 2007 - Manto Negro Black Red 2006, 2007 2 Pampolat girat Black Red 2006, 2007 - Valenci Tinto Black Red 2006 - Eperó and Fogoneu Black Red 2006 Beba roja Rose Rose 2006, 2007 - Giró Black Rose 2007 - Sabaté Black Rose 2007 - Mansés de Tibbus Black Rose 2007 - Beba Green-yellow White 2006, 2007 1 Pensal Blanca Green-yellow White 2006, 2007 2 Quigat Green-yellow White 2006, 2007 -
All the varieties were cultivated at the Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá
de Henares, Spain). Plants were almost five years old and were grown under the same field
conditions. All of them were grafted onto 110R and were irrigated until veraison. It had a unilateral
cordon trellis system and eight buds per vine were left in winter pruning. The distance between
vines was 0.9 m and the one within rows was 2.5 m. A set of 11 agronomical parameters were
studied in vine plants: grape harvested per vine (kg), number of bunches per vine, number of
bunches per shoot, bunch weight, number of berries per bunch, berry weight and must yield were
measured in harvest, whereas number of shoots per vine, Kg of pruning wood per kg of grape,
woody shoots weight per vine and woody shoots weight were measured during vines dormancy
(OIV 2001; Table 2).
Sensory characterization and factors influencing quality of wines
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Table 2 Agronomical parameters of the 18 studied varieties. Data are the media of the 2006 and 2007 vintages. Missing data are scored as “md”
Variety Kg
grape/Vine
Number of bunches/Vine
Number of bunches/Shoot
Kg pruning wood/kg
grape
Total woody shoots
weight/Vine
Woody shoots weight (g)
Number of woody
shoot/Vine
Bunch weight (g)
Number of berries/Bunch
Berry weight
(g)
Must yield (%)
Batista 0,60 7,53 0,84 0,42 0,22 27,60 7,89 170,78 121,17 1,67 30,06
Beba blanca 1,21 5,81 0,71 0,46 0,41 52,75 7,91 222,07 97,00 2,91 24,28
Valenci Tinto 0,48 4,41 0,49 2,56 0,43 56,33 7,48 148,02 75,83 3,38 28,20
Beba roja 0,91 5,65 0,89 0,53 0,37 55,23 6,61 285,70 124,67 2,97 23,60
Bobal 0,75 3,58 0,43 0,37 0,23 23,24 9,80 233,77 134,67 2,41 31,07
Callet 1,16 6,98 0,85 0,15 0,16 17,64 8,59 379,61 199,33 2,39 28,54
Eperó de Gall 0,74 6,86 0,88 0,85 0,43 53,60 8,00 299,66 198,50 2,12 28,26
Excursach 1,15 8,54 1,09 0,98 0,27 30,83 8,25 293,12 180,33 1,74 24,52
Fogoneu 1,10 9,29 1,08 0,51 0,26 21,68 8,77 276,76 158,17 2,34 25,10
Mandón md md md md md md md 366,70 251,83 1,79 27,22
Giró 1,93 9,79 1,13 0,21 0,32 36,79 8,51 298,76 173,08 2,20 31,06
Gorgollassa 0,61 6,83 0,85 0,82 0,31 38,95 7,89 152,90 80,67 2,24 27,35
Mansés de Tibbus 1,78 8,35 0,98 0,39 0,50 59,66 8,52 328,25 195,33 2,32 31,01
Manto Negro 1,10 7,04 0,87 0,26 0,27 33,65 7,89 300,50 180,33 2,05 22,79
Pampolat girat 0,81 6,82 0,86 0,60 0,30 35,51 8,43 223,92 153,67 1,69 29,72
Pensal Blanca 2,36 9,12 1,26 0,19 0,31 35,29 8,26 486,98 223,75 2,52 32,46
Quigat 1,83 9,46 1,32 0,14 0,16 21,76 7,65 378,15 181,50 2,70 21,23
Sabaté 2,14 9,02 1,07 0,15 0,22 27,34 8,15 403,77 316,17 1,68 33,53
Chapter 7
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Winemaking protocol
In this study we analyzed 32 wines including red, white and rose, made from 18 different varieties.
The wines were elaborated in two vintages, 15 wines were made in 2006, and 17 wines in 2007.
Depending of the available quantity of grape harvested, 13 of them were common in the two
vintages (Table 1). All wines were mono-varietal, made from 100% of the same specified variety
except a wine made from a blend of Fogoneu and Eperó de Gall varieties in 2006 (63 and 37%
respectively; Table 1). The grapes were picked by hand, in excellent sanitary conditions, at
commercial maturation during 2006 and 2007 vintages. The fermentation was carried out following
traditional winemaking methods. Grapes were destemmed and slightly crushed using an electric
crusher (CME Construzioni Machine Enologiche, Italia). The musts were treated with sulphur
dioxide liquid (20, 25 and 25 mg L-1 SO2 equivalent for red, white and rose respectively) and
macerated 24h approximately with pectolitic enzyme (Vinozym, Novo Nordisk Ferment Ltd,
Dittingen) at 10ºC. The white and rose musts were pressed using a manual press and then they
were maintained at 10ºC during 12h, after that the musts were racked.
All must were inoculated with yeast Saccharomyces cerevisiae. The yeast Fermol PB 2033,
Fermol Reims Champagne and Fermol Grand Rouge nature (Pascal Biotech, AEB Group, France)
were used for red, white and rose varieties winemaking respectively. They were prepared
according to the manufacturer´s recommendation. Depending of the grape harvest available, all
fermentation were carried out in glass container for must volume less than 17L, and in stainless
steel tank “always full” for more than 17L. The fermentation temperature was maintained between
25 and 30°C for red wines and between 15-20ºC for white and rose wines in a dark air-conditioned
room. In red wines, fermentation caps were punched down twice a day each 12h. In all wines,
fermentations were monitored for temperature and density upon completion of the alcoholic
fermentations indicated by constant density. When the fermentations red wines finished, the wines
were manually racked off and pressed in a manual press. Then they were transferred to glass
recipients according to its final obtained volume. Red wines were inoculated with Oenococus oeni
lactic acid bacteria (Vinifer, Agrovin, Spain) to induce malolactic fermentation. After that, the wines
were racked.
The red wines were clarified with 10 g hL-1, of commercial egg albumin and white and rose
wines with 10 g hL-1 of bentonite. The wines were stored for 7 days at 8ºC in a dark room to allow
its cold stabilisation. Afterwards, the wines were racked again. Prior to the final bottling, sulphur
dioxide was adjusted to 40 mg L-1. The wines were filtered through stainless steel filter holders
(INLET YY3014236, Millipore Bedford, MA, USA) using N2 as carried gas and 0.45µm filter disc
(Millipore, Bedford, MA, USA). Then they were bottled in 750 ml of capacity, labelled and
horizontally stored in a conditioned room kept at 10-12 ºC and 70% air humidity until sensory
analysis, which was carried out around 4 months after bottling in each vintage. At the same time,
Sensory characterization and factors influencing quality of wines
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for each wine, a bottle was set apart for chemical analysis, which was conducted 5 months after
fermentation was completed.
Chemical analysis
Alcohol content (% vol), relative density, dry extract, volatile acidity, total acidity (expressed in g L-1
TH2), pH, free SO2, reducing sugar, total phenolic index, colour intensity, malic acid and tonality
were determined according to European Union Commission Regulation method (RCEE 2676
1990; Table 3). All analyses were run three times and were done in the confirmed IMIDRA
laboratory (UNE-EN ISO/IEC 17025, 2000).
Chapter 7
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Table 3 Chemical analysis of the 32 studies wines. Missing data (md)
Variety Vintage Alcohol
content (%) Relative density
Dry extract
Volatile acidity
Total acidity (gL
-1 TH2)
pH Free SO2 Reducing
sugar Total phenolic
index Colour
intensity Malic acid
Tonality
Beba blanca 2006 13.2 1.0005 19.6 0.67 5.5 3.46 10 3.3 8.51 0.160 1.7 md
Pensal Blanca 2006 12.0 0.9904 15.8 0.37 6.2 3.02 12 2.2 8.12 0.131 0.6 md
Quigat 2006 8.9 0.9921 10.7 0.28 6.4 3.17 13 2.4 8.19 0.163 1.1 md
Beba roja 2006 11.5 0.9922 18.8 0.20 6.20 3.35 21 2.3 8.97 0.187 1.9 md
Batista 2006 13.2 0.9928 22.9 0.55 4.5 3.71 30 2.3 29.88 1.606 0.1 md
Valenci Tinto 2006 14.4 0.9923 27.9 0.41 6.3 3.69 28 1.7 34.53 2.055 1.5 md
Bobal 2006 13.5 0.9920 24.8 0.30 5.6 3.56 32 1.5 40.36 10.332 1.0 md
Callet 2006 15.1 0.9916 30.7 0.33 5.4 3.91 30 1.6 58.32 4.520 2.3 md
Eperó de Gall 2006 17.0 0.9931 37.4 0.84 6.5 3.79 28 1.9 42.10 3.739 2.5 md
Eperó and Fogoneu 2006 13.1 0.9927 25.3 0.57 5.0 3.72 30 2.2 40.43 4.360 0.1 md
Excursach 2006 13.3 0.9924 25.0 0.22 6.3 3.41 27 1.9 42.76 8.706 1.1 md
Fogoneu 2006 12.8 0.9926 24.2 0.37 6.1 3.50 21 2.0 40.58 7.311 0.1 md
Gorgollassa 2006 14.4 0.9912 25.0 0.29 6.2 3.51 28 1.3 42.31 4.011 1.2 md
Manto Negro 2006 16.3 0.9909 27.1 0.36 5.8 3.62 37 2.1 42.25 3.570 1.8 md
Pampolat girat 2006 12.2 0.9932 28.4 0.57 4.3 3.92 25 1.4 57.67 3.850 0.0 md
Sensory characterization and factors influencing quality of wines
- 112 -
Variety Vintage Alcohol
content (%) Relative density
Dry extract
Volatile acidity
Total acidity (g L
-1 TH2)
pH Free SO2 Reducing
sugar Total phenolic
index Colour
intensity Malic acid
Tonality
Beba blanca 2007 13.3 0.9909 21.1 0.55 5.5 3.70 57 2.9 8.02 0.115 2.8 5.197
Pensal Blanca 2007 12.5 0.9913 19.6 0.42 4.9 3.70 23 3.2 11.07 0.097 2.1 4.596
Quigat 2007 12.5 0.9912 19.6 0.24 6.0 3.29 22 2.2 7.91 0.103 2.1 4.404
Beba roja 2007 11.3 0.9931 20.9 0.26 5.7 3.41 22 2.4 6.84 0.144 2.1 3.015
Giró 2007 12.5 0.9939 21.6 0.36 5.3 3.38 30 3.2 10.55 0.469 1.8 1.180
Mansés de Tibbus 2007 14.1 0.9891 18.8 0.48 4.5 3.69 35 1.6 15.19 0.723 2.2 1.203
Sabaté 2007 14.4 0.9891 19.3 0.31 5.4 3.61 40 1.7 13.09 0.709 2.9 1.069
Batista 2007 10.1 0.9949 22.2 0.22 5.3 3.72 39 1.8 40.00 1.337 2.8 1.304
Bobal 2007 12.4 0.9935 25.3 0.22 5.2 3.76 33 1.2 40.40 6.069 0.4 0.777
Callet 2007 13.4 0.9926 25.5 0.45 3.4 4.16 42 1.2 32.22 2.608 0.1 1.352
Eperó de Gall 2007 14.4 0.9925 28.6 0.27 6.7 3.78 28 1.3 31.19 2.697 3.6 1.238
Excursach 2007 14.9 0.9921 28.9 0.29 6.1 3.69 42 1.6 45.87 7.335 2.1 0.787
Fogoneu 2007 12.8 0.9938 26.8 0.43 5.3 3.87 37 1.6 39.62 4.716 0.1 0.927
Mandón 2007 13.8 0.9930 27.9 0.30 6.2 3.64 29 1.3 31.44 3.447 2.4 0.995
Gorgollassa 2007 13.8 0.9936 3.6 0.24 6.2 3.78 28 1.0 31.80 4.586 2.8 0.991
Manto Negro 2007 14.5 0.9917 26.6 0.39 4.1 4.04 29 1.0 28.17 2.324 0.0 1.298
Pampolat girat 2007 15.0 0.9931 31.5 0.38 4.6 4.00 18 1.2 38.37 3.817 0.0 1.284
Chapter 7
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Sensory descriptive analysis
A panel of 21 volunteer tasters from Oenological and Viticulture department belonging to IMIDRA
staff participated in the study (14 male and 7 female). All tasters had previously participated in
wine sensory descriptive analysis studies, so they had great wine-judging experience, however
one of them who was not adjusted for all sensory descriptors was rejected of the panel (data do
not show). Nine of them (6 male and 3 female; mean year=39, range=30-49) who tasted all wines
were chosen to analyze the wines, however the final mark from the 20 volunteers tasters were
used to performance the expert´s internal preference map.
All sessions were conducted at the IMIDRA tasting room in individual booths. The tasting room
was normalized (UNE-EN ISO/IEC 17025, 2000), room temperature of 20–22ºC, 60–70% relative
humidity. Each sample (30 mL) was coded by three random digits, covered with Petri dishes and
served in standard wine tasting glasses in accordance with the International Organization for
Standardization (Norme ISO 3591 1977). The tasting sessions started at 11 am and took
approximately 1h. Six wines at 17 ºC, presented in random order (Norme ISO 3591 1977), were
tasted per session, so three sessions were needed each year. Tasters were asked to first rate the
visual attributes of the wine, followed by odour and oral evaluations, and they were instructed to
expectorate the samples as well as to rinse with mineral water and unsalted crackers to eliminate
residual sensations between samples. Wines were tasted only once since a low quantity wine was
obtained.
The sensory evaluation of the wines was performed by using a questionnaire composed of 21
sensory descriptors grouped in two visual descriptors (limpidity, colour intensity), 14 olfactory
descriptors: three related to aroma limpidity, aroma intensity and aroma fineness, and 11 aroma
terms (citric, green fruit, stone fruit, red fruit, black fruit, tropical fruit, floral, spicy, herbaceous,
animal, lactic), three taste descriptors (sweet, acidity, bitter), and two others descriptors (body,
flavour quality). Finally, the tasters were instructed to give a global mark of each wine according to
their own wine quality perception. All intensity ratings were scored from cero “not perceivable” to
ten “very strong”.
A mixture of both intensity and frequency of aroma terms was calculated using the Modified
Frequencies formula (MF) proposed by Dravnieks (1985) in order to identified the most important
aroma descriptors (Vilanova et al. 2008; Tao et al. 2009). MF=[F(%)*I(%)]1/2, where F (%) is the
detection frequency of an aroma descriptor, and I (%) is the average intensity of that aroma
descriptor expresses as percentage of the maximum intensity of the aroma.
Statistical analysis
The modified frequency of the aroma terms and the mean of the rest sensory descriptors obtained
in 2006 and 2007 vintages were used to performance the Principal Component Analysis (PCA) for
red, white and rose wines. The PCA analysis allowed establishing a relationship between the
Sensory characterization and factors influencing quality of wines
- 114 -
different sensory descriptors and the studied wines, as well as identifying the most important
sensory descriptors in white, rose and red wines. Differences between vintages and between
varieties included and excluded from Designation of Origin were checked by ANOVA followed by
Tuckey´s HSD test to enable pairwise comparisons of means (p<0.05). The modified frequency of
the aroma terms and the mean of the rest sensory descriptors, agronomical parameters and
chemical analysis were used to analyse the data set using Partial Least Squares regression
(PLS), which was applied in order to determinate the relationship between (1) the chemical
analysis and sensory data, and (2) agronomical parameters and sensory data. Aroma terms which
presented a MF<0.05 were omitted for sensory data statistical analysis. The global mark was used
as hedonic measure of the wine quality in order to perform an expert´s preference map, thus it
was possible to know which wine was better positioned in relation to the expert preferences and
so we could be able to know which wine was better accepted by the wine experts. All statistical
analyses were performed using XLSTAT 2009 version.
Results
Sensory descriptions analysis of the tasted wines
Three PCAs were performance following the winemaking protocols (Table 4, Figure 1).
Table 4. Contribution of the variables (%) for the first two axes of the wines characterization following the winemaker protocols. Values > 10% are in bold.
Sensory descriptors
Red wines White wines Rose wines
F1 F2
F1 F2
F1 F2
Citric
11.58 2.72
1.32 14.45
9.18 0.13
Green fruit
1.71 2.55
5.75 4.12
8.50 0.08
Stone fruit
2.84 0.11
7.42 0.22
0.09 8.63
Red fruit
0.03 2.29
0.00 0.00
6.20 3.47
Black fruit
5.29 11.35
0.00 0.00
5.89 5.36
Tropical fruit
4.43 0.04
6.74 1.82
0.74 13.01
Floral
2.42 4.85
7.02 1.17
6.66 0.92
Spicy
0.76 6.21
0.29 16.87
8.49 1.41
Herbaceous
9.27 4.67
6.63 2.06
9.34 0.04
Animal
11.91 0.37
3.76 8.78
6.68 0.80
Lactic
1.32 11.26
5.79 4.04
7.80 0.05
Limpidity
0.10 2.66
1.61 13.78
0.83 12.95
Colour intensity
2.37 14.39
2.50 11.70
2.95 0.04
Aroma limpidity
3.14 2.47
7.51 0.01
3.19 3.03
Aroma intensity
1.71 0.04
6.55 2.25
0.01 12.08
Fineness
3.87 0.97
4.76 6.43
3.90 8.36
Body
0.31 19.42
4.07 8.04
3.89 8.05
Flavour
3.14 11.32
7.14 0.88
4.79 5.33
Sweet
13.19 0.52
7.50 0.05
0.42 3.53
Acidity
10.72 1.52
7.51 0.02
6.86 3.88
Bitter 9.85 0.24 6.12 3.27 3.58 8.84
Chapter 7
- 115 -
Batista
Valenci Tinto
Bobal
Callet
Eperó de gall
Eperó & Fogoneu
Excursach
Fogoneu
Mandón
Gorgollassa
Manto Negro
Pampolat girat
CitricGreen fruit
Stone fruit
Red fruit
Black fruit
Tropical fruit
Floral
SpicyHerbaceous
Animal
Lactic
Limpidity
Color intensity
Aroma limpidity
Aroma intensity
Fineness
Body
Flavour
Sweet
Acidity
Bitter
-4
-3
-2
-1
0
1
2
3
4
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5
F2 (18.0
1 %
)
F1 (24.42 %)
Biplot (axes F1 and F2: 42.43 %)
Figure 1. Principal Component Analysis plots for PCA1 and PCA2 of red wines. Symbols: black circles (grapevine varieties) and grey circles (sensory descriptive analysis). The averages of the 2006-2007 vintages descriptors were used for the analysis
In red wines, axis 1 (24% of the variation, Figure 1) was mainly related to sweet descriptors and
citric aroma term, producing a separation of Valenci Tinto and Manto Negro on the positive end
from Fogoneu and the blend wine made from Eperó de gall and Fogoneu varieties at negative end
of PCA1. The axis 2 (18% of the variation) was related to body and colour intensity, separating
clearly Bobal and Excursach with grater body of the positive end from the spicy varieties Eperó de
gall and Batista at the negative end. Therefore, the first two axes seem to be responding to two
relatively independent gradients: the main one (PCA axis 1) associated with sweetened and
aroma terms and the second one (PCA axes 2) related to visual and structural descriptors. The
red wines, Bobal, Excursach, Mandón, Valenci Tinto and Manto Negro were located in the upper
right of the figure and were mainly related to flavour, sweet and aroma limpidity descriptors. Spicy
and citric descriptors characterized Callet, Batista and Eperó de gall wines, thus they were placed
in the lower right quadrant. Fogoneu, Gorgollassa and the wine made from Eperó and Fogoneu
varieties were located in the lower left quadrant, linked mainly to herbaceous and acidity
descriptors. Pampolat girat wine was characterized by body, colour intensity, black fruit, floral and
bitter descriptors.
Sensory characterization and factors influencing quality of wines
- 116 -
The PCA performed for white wines showed that the most discriminatory descriptors for the
white wines were aroma limpidity, acidity and sweet descriptors in axis 1 (70% of the variation;
Figure 2), and spicy, citric and limpidity in axis 2 (30% of the variation). The first axis seems to be
related to taste and aroma terms and the second one with aroma terms. Beba blanca was mapped
in the lower right quadrant because of aroma limpidity, colour intensity, sweet, body and floral
descriptors, in contrast Quigat, located in the lower left quadrant, was characterized by green fruit
and it showed less aroma and flavor. Finally, Pensal Blanca was related to limpidity, spicy, bitter
and herbaceous descriptors, thus located in the upper left quadrant.
Beba blanca
Pensal Blanca
Quigat
Citric
Green fruit
Stone fruit
Tropical fruit
Floral
Spicy
Herbaceous
Animal
Lactic
Limpidity
Color intensity
Aroma limpidity
Aroma intensity
Fineness
Body
Flavour
SweetAcidity
Bitter
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3 4 5
F2 (29.9
9 %
)
F1 (70.01 %)
Biplot (axes F1 and F2: 100.00 %)
Figure 2. Principal Component Analysis plots for PCA1 and PCA2 of white wines. Symbols: black circles (grapevine varieties) and grey circles (sensory descriptive analysis). The averages of the 2006-2007 vintages descriptors were used for the analysis
Finally, the PCA for rose wines (Figure 3), showed that herbaceous and citric were the most
important descriptors for axis 1 (50% of the variation) and tropical fruit, limpidity and aroma
intensity for axis 2 (33% of the variation). Beba roja was related to tropical fruit, acidity and citric
descriptors. However, Sabaté and Giró were related to limpidity and body, although Giró showed
greater black fruit and red fruit descriptors. Finally, Mansés de Tibbus was characterized by aroma
intensity, fineness and aroma limpidity.
Chapter 7
- 117 -
Beba roja
Giró
Mansés de Tibbus
Sabaté
CitricGreen fruit
Stone fruit
Red fruit
Black fruit
Tropical fruit
Floral
Spicy
HerbaceousAnimalLactic
Limpidity
Color intensity
Aroma limpidity
Aroma intensity
Fineness
Body
Flavour
Sweet
AcidityBitter
-3
-2
-1
0
1
2
3
-5 -4 -3 -2 -1 0 1 2 3 4
F2 (33.3
2 %
)
F1 (50.38 %)
Biplot (axes F1 and F2: 83.71 %)
Figure 3. Principal Component Analysis plots for PCA1 and PCA2 of rose wines. Symbols: black circles (grapevine varieties) and grey circles (sensory descriptive analysis). The averages of the 2006-2007 vintages descriptors were used for the analysis
Effect of vintage and Denomination of Origin (DO) over the chemical analysis
According to ANOVA results for the vintage effect over the chemical analysis, in red wines there
were significant differences for reducing sugar (r2=0.36; p<0.05; 1,75 vs 1.32 for 2006, 2007
vintage respectively) and total phenolic index (r2=0.20; p<0.05; 42.83 vs 35.91 for 2006, 2007
vintage respectively). On the other hand, the white wines showed significant differences in colour
intensity (r2=0.80; p<0.05; 0.151 vs 0.105 for 2006, 2007 vintages respectively) and malic acid
(r2=0.69; p<0.05; 1.13 vs 2.33 for 2006, 2007 vintages respectively). However, no significant
differences had been found between vintages of the rose wines. Also, there were no significant
differences in chemical analyses between varieties included in a Denomination of Origin and those
excluded either in red or white wines.
Effect of vintage and Denomination of Origin (DO) over the sensory analysis
According to ANOVA results, the vintage had significant influence over aroma terms in red wines
for Fogoneu and Gorgollassa, since significant differences (p<0.05) have been found relating with
herbaceous and spicy respectively. Aroma intensity was better scored for Beba roja rose wine in
Sensory characterization and factors influencing quality of wines
- 118 -
2007 (r2=0.30; p<0.05; 4.80 vs 6.80 for 2006, 2007 vintages respectively). However no differences
were found between the sensorial analyses carried out in 2006 and 2007 vintages in white wines.
There were no differences in the characters related to aroma terms, taste descriptors, body or
flavour quality sensory attributes between the varieties included in DO versus those excluded.
Although a comparison between red varieties included in a DO and those excluded no significant
differences have been found, regarding to white wines, only aroma intensity character was
significant different being the wines made from varieties included in DO, Beba and Pensal Blanca
were better scored than the excluded ones (r2=0.11; p<0.05; 6.12 vs 4.89, respectively).
Correlation between chemical analysis, agronomical parameters and sensory
characterization
In order to evaluate the efficiency of the tasters, PLS were applied to evaluate the correlation
between sensory data scored by experts and chemical parameters analysed in the laboratory
which also corroborate the classification of PCA. In general, the data obtained by tasters fitted with
the chemical analysis carried out in the laboratory (correlation coefficients significantly different
from zero, p<0.05, are showed in Tables 5-7).
Table 5. Regression coefficients from the PLS model for the analytical parameters analysed in the laboratory that most contribute in the specific sensory attributes scored by tasters for red wines. Correlation coefficients are in blackest
Sensory attributes Total phenolic index Colour intensity Tonality
Limpidity -2.753 (-0.60) -0.650 (-0.42)
Colour intensity
0.370 (0.97)
Body
0.699 (0.88) -0.057 (-0.71)
Table 6. Regression coefficients from the PLS model for the analytical parameters analysed in the laboratory that most contribute in the specific sensory attributes scored by tasters for white wines. Correlation coefficients are in blackest
Sensory attributes
Alcohol content
(%)
Relative density
Dry extract
Volatile acidity
Total acidity
pH Reducing
sugar Malic acid
Tonality
Aroma limpidity 0.162 (0.92)
0.343 (0.96)
0.024 (0.99)
-0.040 (-0.73)
0.024 (0.99)
0.053 (0.96)
0.056 (0.99)
Aroma intensity 0.111 (0.98)
0.235 (0.99)
0.016 (0.97)
-0.028 (-0.94)
0.016 (0.97)
0.036 (0.99)
0.038 (0.92)
Fineness 0.303 (0.96)
0.638 (0.92)
0.044 (0.87)
-0.075 (-0.99)
0.044 (0.86)
0.098 (0.93)
0.104 (0.78)
Body
0.001 (0.95)
0.052 (0.98)
Flavour 0.200 (0.99)
0.423 (0.99)
0.029 (0.99)
-0.050 (-0.88)
0.029 (0.99)
0.065 (0.99)
0.069 (0.97)
Sweet 0.176 (0.90)
0.001 (0.93)
0.370 (0.95)
0.026 (0.98)
-0.044 (-0.71)
0.026 (0.99)
0.057 (0.95)
0.060 (0.99)
Acidity -0.157 (-0.94)
-0.332 (-0.98)
-0.023 (-0.99)
0.039 (0.77)
-0.023 (-0.99)
-0.051 (-0.97)
-0.054 (-0.99)
Bitter
-0.152 (-0.99)
Chapter 7
- 119 -
Table 7. Regression coefficients from the PLS model for the analytical parameters analysed in the laboratory that most contribute in the specific sensory attributes scored by tasters for rose wines. Correlation coefficients are in blackest
Sensory attributes
Alcohol content (%) Volatile acidity Total acidity Colour intensity Malic acid
Colour intensity 0.172 (0.93)
0.026 (0.85) 0.059 (0.84)
Sweet 0.151 (0.83)
0.023 (0.74)
Acidity
-0.009 (-0.83) 0.036 (0.71)
It is worth mentioning the correlation between colour intensity analysed in the laboratory and
colour intensity scored by tasters for rose and red wines, as well as colour intensity analysed in
the laboratory and body scored by tasters. For white and rose wines, it is highlight the positive
correlations between total acidity analysed in the laboratory and acidity scored by tasters,
reducing sugar analysed in the laboratory and sweet scored by tasters, as well as alcohol content
and sweet. Malic acid and relative density were positively correlated with body attribute in white
wines. However, volatile acidity was negatively correlated with acidity scored by tasters in white
and rose wines as well as pH and acidity attribute for white wine.
With the aim to identify the influence of the agronomical parameters over the sensory wine
analysis PLS were performed. Although no correlations were found between agronomical
parameters and sensory analysis in red wines, several correlations were found in rose and white
wines (correlation coefficients significantly different from zero, p<0.05, are showed in Tables 8, 9).
The PLS revealed negative correlations between number of bunches per vine, number of
bunches per shoot and green fruit, stone fruit, tropical fruit and floral aroma terms scored by
tasters except for green fruit which showed an inverse relationship. However, positive correlations
were found between kilograms of pruning wood per kilograms of grape, total woody shoots weight
per vine and woody shoots weight and these aroma terms except for green fruit scored by tasters
which showed a negative correlation. Although no influence were found between these
agronomical parameters and herbaceous term, which was correlated positively with kilograms of
grape per vine, bunch weight, number of berries per bunch and negatively correlated with berry
weight for white wines, however this attribute was negatively correlated with number of woody
shoots per vine in rose wines.
Sensory characterization and factors influencing quality of wines
- 120 -
Table 8. Regression coefficients from the PLS model for the agronomical parameters that most contribute in the specific sensory attributes scored by tasters for white wines. Correlation coefficients are in blackest
Sensory attributes
Kg grape/Vine Number of
bunches/Vine Number of
bunches/Shoot Kg pruning
wood/kg grape
Total woody shoots
weight/Vine
Woody shoot weight
Bunch weight Number of
berries/Bunch Berry weight
Citric
-165.645 (-0.90) -84.563 (-0.86)
Green fruit
2.074 (0.78) 0.348 (0.78) -0.181 (-0.81) -0.116 (-0.99) -15.335 (-0.95)
Stone fruit
-1.510 (-0.99) -0.254 (-0.99) 0.132 (0.99) 0.085 (0.90) 11.167 (0.95)
Tropical fruit
-1.137 (-0.87) -0.191 (-0.88) 0.099 (0.90) 0.064 (0.99) 8.405 (0.99)
Floral
-3.387 (-0.99) -0.569 (-0.99) 0.295 (0.99) 0.190 (0.82) 25.047 (0.90)
Herbaceous 0.842 (0.92)
201.992 (0.94) 103.119 (0.97) -0.288 (-0.93)
Lactic
-2.179 (-0.78) -0.366 (-0.79) 0.190 (0.82) 0.122 (0.99) 16.111 (0.95)
Limpidity 0.089 (0.94)
21.366 (0.92) 10.908 (0.88) -0.030 (-0.94)
Colour intensity -0.044 (-0.98)
-10.609 (-0.96) -5.416 (-0.94) 0.015 (0.98)
Aroma limpidity
-0.273 (-0.98) -0.046 (-0.99) 0.024 (0.99) 0.015 (0.93) 2.019 (0.98)
Aroma intensity
-0.165 (-0.86) -0.028 (-0.86) 0.014 (0.89) 0.009 (0.99) 1.222 (0.98)
Fineness
-0.397 (-0.68) -0.067 (-0.69) 0.035 (0.72) 0.022 (0.95) 2.932 (0.90)
Body -0.078 (-0.99)
-18.623 (-0.99) -9.507 (-0.99) 0.027 (0.99)
Flavour
-0.312 (-0.92) -0.052 (-0.92) 0.027 (0.943) 0.017 (0.99) 2.306 (0.99)
Sweet
-0.297 (-0.99) -0.050 (-0.99) 0.026 (0.99) 0.017 (0.92) 2.199 (0.97)
Acidity
0.260 (0.98) 0.044 (0.98) -0.023 (-0.99) -0.015 (-0.95) -1.921 (-0.99)
Bitter 0.204 (0.96)
48.875 (0.97) 24.951 (0.99) -0.070 (-0.96)
Chapter 7
- 121 -
Table 9. Regression coefficients from the PLS model for the agronomical parameters that most contribute in the specific sensory attributes scored by tasters for
rose wines. Correlation coefficients are in blackest
Sensory attributes
Kg grape/Vine Number of
bunches/Vine Number of
bunches/Shoot Kg pruning
wood/kg grape
Total woody shoots
weight/Vine
Number of woody
shoot/Vine
Bunch weight
Number of berries/Bunch
Berry weight Must yield
Citric
-0.455 (-0.88)
Red fruit
41.988 (0.65) 76.381 (0.71)
Black fruit
0.916 (0.99)
Tropical fruit
-2.460 (-0.40) -0.146 (-0.99)
Floral
-1.909 (-0.85)
Herbaceous
-1.346 (-0.81)
Limpidity
0.324 (0.60) 0.023 (0.82)
Aroma limpidity
0.877 (0.92)
Aroma intensity
-0.311 (-0.75)
Fineness
-0.016 (-0.52) 0.033 (0.54)
Body
0.397 (0.98) 0.021 (0.92)
0.191 (0.94)
Flavour
0.064 (0.98)
0.812 (0.79)
Sweet 0.077 (0.77)
11.129 (0.99) -0.083 (-0.91)
Acidity
-0.120 (-0.99)
Bitter
-0.028 (-0.55) 0.057 (0.57)
Sensory characterization and factors influencing quality of wines
- 122 -
Number of bunches per vine was negatively correlated with aroma intensity in white and rose
wines. In white wines, number of bunches per vine and number of bunches per shoot were also
negative correlated with sweet. Body attribute scored by tasters was negatively correlated with
kilograms of grape per vine for white wines, however in rose wines this attribute was positively
correlated with number of bunches per vine and number of bunches per shoot.
Analysis of the preference map
Tasters quantified each wine given a global mark which was used as hedonic measure of the wine
quality. Therefore, it was possible to map the wines according to expert´s preferences. As a
consequence, two preferences maps were performed one for red wines and other one for white
and rose wines. In red wines, four clusters were performed (Table 10, Figure 4).
Table 10. Wines sorted by increasing expert´s preference order in red wines
Cluster1 Cluster2 Cluster3 Cluster4
Eperó de gall Fogoneu Fogoneu Eperó de gall
Batista Eperó&Fogoneu Eperó&Fogoneu Batista
Callet Gorgollassa Gorgollassa Callet
Eperó&Fogoneu Pampolat girat Pampolat girat Eperó&Fogoneu
Manto Negro Eperó de gall Mandón Gorgollassa
Gorgollassa Callet Bobal Manto Negro
Fogoneu Batista Excursach Fogoneu
Valenci Tinto Mandón Callet Valenci Tinto
Mandón Manto Negro Eperó de gall Mandón
Pampolat girat Excursach Manto Negro Pampolat girat
Excursach Bobal Batista Excursach
Bobal Valenci Tinto Valenci Tinto Bobal
The most preference wines were Bobal and Excursach for cluster 1 and 4, however Valenci
Tinto and Bobal were better scored by Cluster 2 and Valenci Tinto and Batista by cluster 3.
Therefore the wines Bobal (allowed in six Spanish DOs), Excursach, Valenci Tinto and Mandón
were located in the better profile of the expert’s preference (80-100% preference). In contrast two
wines made from varieties allowed in Majorcan DOs, Manto Negro and Callet, were mapped in the
40-60% of the experts preferences, as well as Pampolat girat wine made from a not allowed
variety in Spanish DOs. It is worth comment that Fogoneu wine was showed the lowest rating
among the variety included in Majorcan DOs. It was a bit better located in the preference map
than Gorgollassa and Eperó de gall. However, the wine made with the blend of Eperó de Gall and
Fogoneu varieties was rejected by experts, since it was the less preferred, 0-20%.
Chapter 7
- 123 -
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
0%-20% 20%-40% 40%-60% 60%-80% 80%-100%
Figure 4. Preference mapping and contour plot for red wines projected on PCA axes. Symbols: black circles (grapevine varieties) and black squares (different expert´s clusters). The averages of the 2006-2007 vintages descriptors were used for the analysis
According with expert´s preference map performed from white and rose wines, three clusters
were obtained, small different between them were detected. The first and second cluster preferred
Beba blanca and Mansés de Tibbus wines, however the third cluster preferred the wines made
from Mansés de Tibbus and Giró (Table 11, Figure 5). Therefore Mansés de Tibbus rose wine
was the only one located in the better position of the expert´s preferences (80-100% preference).
Beba blanca wine, variety allowed in one Spanish DO, located in the lower right quadrant,
meaning that 60-80% of the experts accepted this wine. Giró and Sabaté rose wines were
mapped in the 20-40% of the experts preference map. Pensal Blanca, variety allowed in Majorcan
DOs, was accepted by around the 40% of the expert, since this wine was located in the border
between 20-40% and 40-60% of the contour plot. Giró and Sabaté wines were preferred by 20-
40% of the experts. Nevertheless, Beba roja and Quigat were the wines rejected by experts.
Table 11. Wines sorted by increasing expert´s preference order in white and rose wines
Cluster1 Cluster2 Cluster3
Giró Beba roja Pensal Blanca
Beba roja Quigat Quigat
Sabaté Giró tinto Beba blanca
Quigat Sabaté Beba roja
Pensal Blanca Pensal Blanca Sabaté
Mansés de Tibbus Mansés de Tibbus Giró
Beba blanca Beba blanca Mansés de Tibbus
Batista
Bobal
Callet
Eperó de gall
Eperó & Fogoneu
Excursach
Fogoneu
Mandón
Gorgollassa
Manto Negro
Pampolat girat Valenci Tinto
Cluster 1
Cluster 2
Cluster 3
Cluster 4
-4
-3
-2
-1
0
1
2
3
4
-6 -4 -2 0 2 4
F2
F1
Sensory characterization and factors influencing quality of wines
- 124 -
0%-20% 20%-40% 40%-60% 60%-80% 80%-100%
Figure 5. Preference mapping and contour plot for white and rose wines projected on PCA axes. Symbols: black circles (grapevine varieties) and black squares (different expert´s clusters). The averages of the 2006-2007 vintages descriptors were used for the analysis
Discussion
Sensory descriptions analysis of the tasted wines
Our results reveal that tasters differentiated the wines according to sensory attributes as it has
been appointed in other studies (Lund et al. 2009; Muñoz-González et al. 2011). However,
according to PCA result, olfactory, taste and visual characters showed different weight for red,
white and red wines. Taste attributes were more significant in red wines, mainly because these
attributes are easier to asses than the aroma ones (Koussissi et al. 2003). In contrast, the
attributes related to aroma terms, mainly citric, spicy and herbaceous ones, were more significant
for white and rose wines as it has been appointed by other authors for different wines (Koussissi
et al. 2003; Nurgel et al. 2004; Scacco et al. 2007). Thus, as a view of these results, the
questionnaire could be quantified efficient for the tasted wines.
Effect of vintage and Denomination of Origin (DO) over the chemical and sensory
analysis
Good reproducibility of chemical and sensory analysis results was obtained for the two vintages,
despite of the vintage influenced over both analyses. In this study, significant differences have
been found in analytical data between the two studied vintages for reducing sugar, total phenolic
index in red wines, and colour intensity and malic acid in white wines out 12 chemical data
analyzed. However, it seems that these differences have not been high enough to be scored by
experts in the sensorial analysis. On the other hand, differences between wines sensorial analysis
Beba blanca
Beba roja
Giró
Mansés de Tibbus
Pensal Blanca
Quigat
Sabaté
Cluster 1
Cluster 2
Cluster 3
-5
-4
-3
-2
-1
0
1
2
3
-6 -4 -2 0 2 4F
2
F1
Chapter 7
- 125 -
between the two vintages have been also established in this work as well it has been appointed by
other authors (Fischer et al. 1999; Vilanova et al. 2008). These aroma attributes were related to
aroma intensity in white wines and two aroma terms scored by tasters for Fogoneu and
Gorgollassa red wines.
Relating to the influence of the DO over wines analyses, only aroma intensity out of 21 sensory
attributes analyzed showed significant different between white wines made from varieties included
in DO for those excluded. These results are in agree with other authors who have appointed that
differences between wines including or not in a Quality Demarcation are not easy to assess
(Maitre et al. 2010). Therefore, the effect of vintage is more important than the Denomination of
Origin over both chemical and sensory characterization for this group of wines.
Correlation between chemical analysis, agronomical parameters and sensory
characterization
Despite the tasters were not training, there was a correlation between wine parameters analyzed
in the laboratory and those scored by the tasters as sweet, acidity or colour intensity, fitted
thoroughly overcoat in white and rose wines. These results emphasize the good outcome obtained
by expert´s tasters (Perrin et al. 2007). However, the correlations in the attributes related to taste
were not stronger in red wines; as a consequence of the interactions between phenolic
compounds and mouth-fell properties (Vidal et al. 2004; Preys et al. 2006). In contrast, the
correlations showing a value over 71% between taste parameters and those obtained by
instrumental analysis as colour intensity-colour intensity, total acidity-acidity, pH-acidity, reducing
sugar-sweetness, were in line with other studies (Boselli et al. 2004; Blackman et al. 2010; Sáenz-
Navajas et al. 2010). However, the correlation between alcohol content and sweetness have not
been appointed for Blackman et al. (2010) or Sáenz-Navajas et al. (2010), conversely to Zamora
et al. (2006), these discrepancies could be due to the different alcohol content and reducing sugar
among the studied wines. The relationship between colour intensity and body is linked to the
phenolic content since these compounds have a direct effect on the sensation of weigh or body
(Jackson 2008).
There were no correlations between agronomical characterization and sensorial analysis in red
varieties, suggesting that it might be caused by the high variability of the studied varieties.
However, in white and rose varieties we found a strong relationship between agronomical
characterization and sensory attributes. The studies relating agronomical data with wine sensory
characterization are not so numerous, although field parameters as yield (Chapman et al. 2004),
vine density (Koussissi et al. 2008), vineyard mechanization (Diago et al. 2010) or water status
(Hakimi Rezaei and Reynolds 2010) have been pointed out to be related to wine quality. Sensorial
analysis of wines respond to yield management (Chapman et al. 2004), since herbaceous aroma
term and flavour were related to kg of grape per vine as well as flavour was linked to number of
Sensory characterization and factors influencing quality of wines
- 126 -
bunches per vine and number of bunches per shoot, these results are in concordance with
Chapman et al. (2004) who found similar results for Cabernet-Sauvignon wines in Napa Valley
(California, EEUU). In this study, it is proved that the vegetative (hence “total woody shoots weight
per vine” and “woody shoot weight”) and productive parameters (hence “number of bunches per
vine” and “number of bunches per shoot”) influence inversely over aroma and taste scored by
experts, and also their balance (hence “Kg pruning wood per kg grape”) are linked to the wines
sensory attributes. All of these parameters are linked directly with must characteristics, as it is to
be expected to final wine (Jackson 2008).
Analysis of the preference map
The utility of the preference map has been probed in wine to map the wines according to tasters
scores (Lund et al. 2009). In this study, it was possible to map the wines according to expert´s
preferences. Therefore, the varieties located in the best position of the expert preference map
should be considered by technicians due to their quality, sometimes even better mapped than the
wines done from varieties allowed in Spanish DOs. The wines made from varieties not included in
DOs had sensorial and chemical results close to the wines made from varieties included in
Spanish DOs.
Conclusions
Both chemical and sensory characterizations were influenced by vintage and Denomination of
Origin effects, however vintage effect was more significant over this group of wines. Therefore
important differences have not been found between sensory study of wines made from minor
varieties not included in Denomination of Origin and those excluded.
This study corroborates the idea that it necessary to achieve a balance between vegetative and
productive growth, since not only the productive, also the vegetative parameters, sways over the
final wine sensorial characterization, which indicates that the management of these parameter
might modify the original sensory profile. Further studies are needed to improve the knowledge of
the way of how agronomical parameters influence over the final wine quality.
In summary, in this work, which is the first sensorial analysis of these minor varieties, highlights
the good acceptation of these wines by wine experts, since the tasters scored the wines made
from minor varieties in similar way as the varieties included in Denomination of Origin, as the
cases of Excursach, Mandón or Pampolat girat for red wine. Therefore, the possibilities of the
minor varieties for the wine market have been shown. This information would gain a more interest
of minor or autochthonous wine varieties being key for their preservation.
Chapter 7
- 127 -
Acknowledgements
Financial support from INIA project RTA 04 175-C3-3. Sonia García Muñoz was supported by a
PhD scholarship from INIA. We thank to Josu G. Alday for statistical advice. Special thanks to the
tasters involved in the sensorial analysis of the wines and to Laboratorio Enológico (IMIDRA
Alcalá de Henares, Madrid) for chemical analysis.
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Douglas D., Cliff M.A., Reynolds A.G. (2001) Canadian terroir: characterization of Riesling wine
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Fischer U., Roth D., Christmann M. (1999) The impact of geographyc origin, vintage and wine
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García-Muñoz S., Lacombe T., de Andrés M.T., Gaforio L., Muñoz-Organero G., Laucou V., This
P., Cabello F. (2011) Grape varieties (Vitis vinifera L.) from the Balearic Islands: genetic
characterization and relationship with Iberian Peninsula and Mediterranean Basin. Genet
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Chapter 8
Trapped
Synthesis
Chapter 8
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Synthesis
Based on historical references, one of the most interesting results was the high grapevine diversity
found in the Balearic Islands despite of its small geographic area. The islands are an important
stronghold for several cultivars conserving several unique genotypes (Chapter 2 and Chapter 4).
However, the loss of grapevine diversity has been quantified for the first time, highlighting an
important loss around 50% of grapevine diversity in around 300 years. This problem is also
common in other viticulture areas (Sladonja et al. 2007; Carimi et al. 2010). Unfortunately,
nowadays some of the antique cultivars are under risk of extinction, as a consequence it is
recommended almost the maintenance of the cultivars in germplasm repositories before the loss
of the antique cultivars is irreversible.
The main causes of grapevine diversity loss appointed in the Balearic Islands, as powdery
mildew (Erysiphe necator Schwein) or the appearance of the phylloxera plague, have been also
highlighted by others authors (Martínez and Pérez 2000; This et al. 2006). In any case, the
greatest change over the Balearic viticulture was dated before the phylloxera plague arrived to the
islands contrary to what was thought. The great Balearic viticulture change was related to the
phylloxera crisis in France; during this period some varieties as Callet and Manto Negro were
favoured due to their high production (Chapter 2), these cultivars have been conserved until
nowadays. Also just before and just after the phylloxera plague arrived to the islands, there were a
high number of accessions which could not been identified. These results confirm the theory about
the lack of ampelography knowledge (Aradhya et al. 2003), partially due to the foreign varieties
introduced from the Iberian Peninsula (as Garnacha, synonymy Grenache; Carretero et al. 1875)
or from France (as Cinsaut; Satorras 1892) during a relative short time period. Before the
phylloxera plague arrived to the islands, the cultivars were introduced mainly from France
(Ballester 1911) to satisfy the new demands of the wine consumers, whereas some cultivars were
introduced to reconstruct the disappeared vineyard area after the arrival of the phylloxera plague.
For these reasons, numerous synonyms and homonyms have been found (Chapter 2, Chapter 3,
Chapter 4), some of them caused by orthographic or phonetic name variations (Chapter 2) as
other authors has been appointed (Cipriani et al. 2010). Antique references have been found for
all the studied accessions and all of them sited the varieties in the Balearic Islands, so it seems to
be proved the relationship between the studied cultivars and the Balearic Islands (Chapter 2,
Chapter 4).
The combination of the ampelographic and genetic characterization has allowed the
identification of the studied accessions (Chaper 3, Chapter 4). As well, the ampelographic
descriptions have been key to identify the references found in the antique bibliography (Chaper 2),
since more than 75% of the found cultivars have been identified using this technique.
Synthesis
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The use of ampelographic descriptions was also the key for the differentiation between Beba
blanca and Beba roja based on berry colour (white and rose respectively). Beba blanca could be
considered a somatic mutant from Beba roja, therefore these two cultivar should be considered as
different cultivars (Laiadi et al. 2009). The ampelographic descriptions have been appointed to be
not objective (Dettweiler 1993), however the results obtained in this research point in opposite
direction, due to the high reproducibility level of the ampelographic results (Chapter 3), and it has
been also demonstrated that only three characters, colour of the young leaf’s upper side (OIV-
051), juiciness of the flesh (OIV-232) and firmness of the flesh (OIV-235) out of the 46 qualitative
parameters studied were not easy to asses by the ampelographers. However, the increased of the
ampelographer’s experience, as it has been appointed by Ortiz et al. (2004), and the correct
selection of the plant material are factors that improved the correct ampelographic descriptions.
The genetic characterization, using microsatellite markers (SSR), establishes a strong genetic
relationship between all the studied cultivars, possible due to the isolation of the geographic area,
as it has been observed in other islands species (Sales et al. 2001). This genotypical fact could be
also related to the high phenotipical similarity showed by the studied cultivars (Chapter 3,
Appendix II).
Despite it is difficult to establish precisely the origin of the grapevine cultivars due to the high
material exchange (This et al. 2006), it seems that the dispersal of the studied varieties are related
with historical human movements and migrations (Costantini et al. 2005). The material exchange
in the Balearic Islands occurred in three periods, the first one around VII century related to Islam
expansion, the second one around XIII-XV centuries related to the expansion of the Kingdom of
Aragón (current Spain) over the Mediterranean Basin, and the third one in the XIX century related
to phylloxera crisis (Chapter 2, Chapter 4). The historical references and the use of the
microsatellite analysis of some of the studied varieties has allowed to prove the origin of some
cultivars, as for Callet or Manto Negro, however the origin of other varieties has been discussed,
as Beba or Excursach, and unfortunately, the origin of certain varieties remaining unknown
(Chapter 2, Chapter 4). The use of microsatellite analysis has also confirmed the existence of two
gene pools, one of them related to Callet cas Concos, an unique genotype collected in a
prospection in the Balearic Islands, standing out the high value of the unknown cultivars.
In order to know the possibilities of the minor varieties, the agronomic behaviour has been
studied. The cultivars have shown a wide range of possibilities in their both agronomic and
oenological characterization. Vinegrowers and winemakers can select grapevine cultivars more or
less productive, with several must characterizations or with different grape potential aroma to
produce wines with diverse final characteristics (Chaper 6, Chapter 7, Appendix III). Several
varieties, which are not allowed in any Demarcation of Quality, have shown high resistance to
powdery mildew (Erysiphe necator Schwein.) on both bunches and leaves (Chapter 5). This
Chapter 8
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character seems to be important for the conservation of these cultivars, since the minor varieties
conserved in the Balearic Islands have shown high resistance to powdery mildew. Therefore it is
possible to select cultivars with different susceptibility. The selection of more resistant cultivars to
powdery mildew implies the reduction of control treatments against this disease.
The typicality of wines is influenced by a large number of factors, as grapevine (Esti and
Tamborra 2006), environmental conditions (Ribéreau-Gayon et al. 2006), agronomical
management (Chapman et al. 2004) or vintage (Maitre et al. 2010). All of these factors are also
related to final wine quality and have been analyzed in this study.
Grapevine cultivar influenced the typicality of wines based mainly on two aspects: the grape
colour of the cultivar, and grape aroma characterization (Chapter 6). The relationship between
grape variety and wine obtained has been previously proved (Ugliano and Moio 2008). However in
this study the wine made from white and red varieties can be clearly differentiated based on
different aroma composition of grape cultivars (Chapter 7).
The differences between red and white grapevines are mainly caused by a mutation on two
genes (Walker et al. 2007), and it seems that this mutation play also an important role in the
pathways to synthesize several aromatic compounds, as benzenic which seems to be lead with
the synthesis of polyphenols (Haslam 1998; Dudareva et al. 2006), or terpenic compounds which
has been appointed to have a genetic component (Chen et al. 2011; Chapter 6). These
compounds have been key between black and white grape cultivars differentiation. When several
accessions of Beba and Giró varieties were studied, differences among their aromatic profiles
were found. Therefore, it seems possible to select clones with higher aroma potential to obtain
wines with a different aroma profile (Botelho et al. 2010). The terpene compounds were also key
to the differentiation of the origin of several accession of Beba based on aroma characterization.
These differences between grapevine clones based on aroma compounds has been also
appointed by other authors (Botelho et al. 2010), as well as the influences of the geographic origin
over the aroma compounds in must (Mulet et al. 1992; Zamuz and Vilanova 2006). Therefore, the
genetic differentiation between red and white cultivar might be also related to the different grape
aroma compound found in white and red varieties.
Related to the environmental conditions, the effect of hailstorm phenomenon was also studied,
underlining its influenced over the agronomic characterization. Hailstorm reduced Kilograms of
grape per vine, number of bunches per shoot and must pH, and increased woody shoot weight
and total acidity (Chapter 3). Some wine sensorial attributes seem to be also related to agronomic
parameters, such as tropical fruit aroma term or aroma intensity, which were influenced by
Kilograms of grape per vine, number of bunches per shoot and total woody shoots weight per vine
(Chapter 7). It is proved that the vegetative parameters, such as total woody shoots weight per
vine and woody shoot weight, and productive parameters such as number of bunches per vine
Synthesis
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and number of bunches per shoot, influenced inversely over aroma and taste scored by wine
experts, pointing out that balance between vegetative and productive growth might exist.
Therefore the management of these agronomic parameters might modify the original sensory
profile (Chapter 7). These results suggest that environmental conditions and agronomic
management are related with final wine quality.
Despite of the vintage influenced over both chemical and sensory analysis, good reproducibility
of the results was obtained for the two vintages analyzed in this study. The vintage influenced over
both wine chemical and sensory analysis. Similar results have been also appointed by other
authors (Fischer et al. 1999). In this study, wine chemical analysis, such as reducing sugar or total
phenolic index in red wines, and colour intensity and malic acid in white wines, were influenced by
the vintage. However, it seems that these differences have not been high enough to be scored by
experts in the sensorial analysis. On the other hand, related to sensory analysis, differences
between sensorial wines analysis between two vintages have been also established, such as
aroma intensity in white wines and two aroma terms scored by tasters for Fogoneu and
Gorgollassa red wines (relating with herbaceous and spicy respectively; Chapter 7).
Beside the quality of wine is difficult to assay (Charters and Pettigrew 2007), quantitative and
descriptive analysis are the most used methods in wine sensory characterization (Murray et al.
2001). In this study, wine sensory characterization was carried out over wines made from 18 minor
varieties. Consequently, based on expert wine scores, it was possible to map the wines according
to their preferences. Therefore, the varieties located in the best position of the expert preference
map should be considered by technicians due to their quality.
Other parameter studied was the effect of the inclusion or not of a variety in a Denomination of
Origin (Chapter 6, Chaper 7). Related to the grape aroma profile, some minor varieties excluded
from DO (i.e. Argamusa, Gorgollassa and Pampolat girat) appeared to be more aromatic than the
varieties included in DO. However, the wines made from varieties not included in DOs had
sensorial and chemical results close to the wines made from varieties included in Spanish DOs,
since no differences were found related to wine chemical analysis. Also only aroma intensity out of
21 sensory attributes analyzed showed significant differences between white wines made from
varieties included in DO than those excluded. Taking into account the scores given by the wine
experts in sensory characterization, the wines made from minor varieties were sometimes even
better mapped (as Excursach of Mandón) than the wines done from varieties allowed in Spanish
DOs (as Fogoneu). These results are in agree with other authors who have appointed that
differences between wines included or not in a Quality Demarcation are not easy to assess (Maitre
et al. 2010). As a consequence, minor varieties could be taken into consideration for the
development of new wine market strategies, which could also play an important role in the
conservation of these cultivars.
Chapter 8
- 135 -
Recommendations for practices
Historical references and microsatellite analysis clarify the relationship between cultivar and site,
which is the link between “terroir” and historical background. Agronomic characterization and
resistance to powdery mildew give an idea about the agronomic behaviour. Aromatic grape and
chemical wine characterization, as well as sensory analysis of the made wines with wine expert
preferences conditioned the oenological potential of the studied cultivars. On the basis of these
results seven cultivars could be recommended to improve the wine quality and conservation of the
viticulture heritage of the Balearic Islands. These cultivars, in order of preference are Argamusa
and Quigat for white vinifications, Gorgollassa, Pampolat girat, Mandón and Excursach for red
vinifications and Mansés de Tibbus for rose vinifications.
References
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Carretero E., Estelrich P., Ferrá B., Gralla, Monlau J., Pou L., Oliver G., Santandreu P., Taronjí J.,
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Chapman D.M, Matthews M.A., Guinard J.X. (2004) Sensory attributes of Cabernet Sauvignon
wines made from vines with different crop yields. Am J Enol Vitic 55: 325-334.
Charters S., Pettigrew S. (2007) The dimensions of wine quality. Food Qual Prefer 18: 997-1007.
Chen F., Tholl D., Bohlmann J., Pichersky E. (2011) The family of terpene synthases in plants: a
mid-size family of genes for specialized metabolism that is highly diversified throughout the
kingdom. Plant J 66: 212-229.
Cipriani G., Spadotto A., Jurman I., Di Gaspero G., Crespan M., Meneghetti S., Frare E., Vignani
R., Cresti M., Morgante M., Pezzotti M., Pe E., Policriti A., Testolin R. (2010) The SSR-based
molecular profile of 1005 grapevine (Vitis vinifera L.) accessions uncovers new synonymy and
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Costantini L., Monaco A., Vouillamoz J.F., Forlani M., Grando M.S. (2005) Genetic relationships
among local Vitis vinifera cultivars from Campania (Italy). Vitis 44: 25-34.
Dettweiler E. (1993) Evaluation of breeding characteristics in Vitis. Influence of climate on
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Dudareva N., Negre F., Nagegowda D.A., Orlova, I. (2006) Plant Volatiles: Recent advances and
future perspectives. Crit Rev Plant Sci 25: 417-439.
Esti M., Tamborra P. (2006) Influence of winemaking techniques on aroma precursors. Anal Chim
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Fischer U., Roth D., Christmann M. (1999) The impact of geographyc origin, vintage and wine
taste on sensory properties of Vitis vinifera cv. Riesling wines. Food Qual Pref 10: 281-288.
Haslam E. (1998) Practical Polyphenolics. From structure to molecular recognition and
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Laiadi Z., Bentchikou M.M., Bravo G., Cabello F., Martinez-Zapater J.M. (2009) Molecular
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Maitre I., Symoneaux R., Jourjon F., Mehinagic E. (2010) Sensory typicality of wines: how
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Martínez M.C., Pérez J.E. (2000) The forgotten vineyard of the Asturias Princedom (North of
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Mulet A., Berna A., Forcen M. (1992) Differentiation and grouping characteristics of varietal grape
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future. Food Res Int 34: 461-471.
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Chapter 9
At the end of the road
Conclusions
Chapter 9
- 139 -
Conclusions:
1.- High grapevine diversity (Vitis vinifera L.) has been found in the Balearic Islands, despite of its
small geographic area. The material exchange in the Balearic Islands occurred in three periods,
the first one around VII century related to Islam expansion, the second one around XIII-XV
centuries related to the expansion of the Kingdom of Aragón (current Spain) over the Occidental
Mediterranean Basin, and the third one in the XIX century related to phylloxera crisis.
2.- The loss of grapevine diversity is evident; half of the identified cultivars growing in natural
conditions have been lost in approximately 300 years. The high rate of grapevine loss needs to
provide roles of conservation. Special attention is recommended for the unique genotypes not
found around the world out of the Balearic Islands. They must to be preserved in grapevine
resources before the loss of these cultivars is irreversible.
3.- A high reproducibility level of the ampelographic results has been found. Only three characters:
colour of the young leaf’s upper side (OIV-051), juiciness of the flesh (OIV-232) and firmness of
the flesh (OIV-235) out of the 46 qualitative parameters studied were not easy to asses by the
ampelographers. The increased of the ampelographer’s experience and the adequate selection
of the plant material are factors that improve the correct ampelographic description.
4.- Three new synonyms around the Mediterranean Basin have been found: Excursach (Balearic
Islands, Spain) matched with Murescu (Corsica, France); Pampolat girat (Tarragona, Spain) with
Cruixent (Corsica, France), and Giró Ros (Balearic Islands; Spain) with Giro sardo (Sardinia,
Italy).
5.- Two lineages (kin-groups) were pointed out, the first one related with Callet Can Concos variety
and the second one with Hebén variety. Thirteen putative parentages have been obtained; six of
them showed full compatibility for all nuclear microsatellites studied and for the chlorotype of
both parents (Callet, Eperó de Gall, Gafarró, Manto Negro, Unknown 1 and Viñaté).
6.- The combination of ampelography, microsatellite analysis and synthesis of historical references
of the cultivars has shown to be excellent tools for a good identification of grapevine material and
to establish a correct regional viticulture.
7.- Thirty three out 159 varieties studied have shown high resistance to powdery mildew (Erysiphe
necator) in both bunch and leaf, being 10 cultivars from the Balearic Islands. Beba blanca was
the only variety from the Balearic Islands which showed high susceptibility in both bunch and leaf
to this disease.
8.- Cultivar, berry colour, and cultivar adaptations to location and environmental conditions play an
important role in the metabolic pathways to obtain the aromatic compounds. These metabolic
pathways seem to have a genetic component.
Conclusions
- 140 -
9.- Based on the results obtained in this study related with the relationship between cultivar and
site (hence historical references and microsatellite analysis), agronomic behaviour (hence
agronomic characterization and resistance to powdery mildew), and oenological potential of the
studies cultivars (hence aromatic grape and chemical wine characterization, sensory analysis of
the made wines and wine expert preferences) seven cultivars could be recommended for the
improvement of wine quality and conservation of the viticulture heritage of the Balearic Islands.
These cultivars, in order of preference are: Argamusa and Quigat for white vinifications,
Gorgollassa, Pampolat girat, Mandón and Excursach for red vinifications and Mansés de Tibbus
for rose vinifications.
Chapter 9
- 141 -
Conclusiones:
1.- Se ha encontrado una elevada diversidad genética de variedades de vid (Vitis vinifera L.) en
las Islas Baleares a pesar de su reducida área geográfica. El intercambio de material vegetal en
las Islas Baleares se ha producido en tres periodos, el primero alrededor del siglo VII
relacionado con la expansión del Islam, el segundo entre los siglos XIII-XV, relacionado con la
expansión del Reino de Aragón en el Mediterráneo occidental, y el tercero sobre el siglo XIX
relacionado con la crisis filoxérica.
2.- La pérdida de diversidad de variedades es evidente, en aproximadamente 300 años se ha
perdido en condiciones naturales la mitad de las variedades de vid que se cultivaban
históricamente. Esta elevada pérdida de variedades obliga a la recomendación de políticas de
conservación. Los genotipos únicos que no se encuentran en otras partes del mundo fuera de
las Islas Baleares deben ser conservados en bancos de germoplasma antes de que la pérdida
de estas variedades sea irreversible.
3.- Los resultados de descripciones ampelográficas han mostrado un elevado nivel de
reproducibilidad. Sólo tres caracteres, correspondientes a color del haz de la hoja joven (OIV-
051), suculencia de la pulpa (OIV-232) y firmeza de la pulpa (OIV-235), de los 46 parámetros
cualitativos estudiados no fueron fáciles de evaluar por los ampelógrafos. La experiencia de los
ampelógrafos y la correcta selección del material son factores que mejoran la descripción
ampelográfica.
4.- Se han encontrado tres nuevas sinonimias alrededor de la cuenca mediterránea: Excursach
(Islas Baleares, España) coincide con Murescu (Córcega, Francia); Pampolat girat (Tarragona,
España) con Cruixent (Córcega, Francia), y Giró Ros (Islas Baleares, España) con Giro sardo
(Cerdeña, Italia).
5.- Se han encontrado dos linajes (“kin-groups”), el primero relacionado con la variedad Callet Can
Concos y el segundo con la variedad Hebén. Se han obtenido también 13 posibles parentescos,
seis de ellos han mostrado compatibilidad completa para ambos parentales en todos los
microsatélites nucleares y clorotípicos estudiados (Callet, Eperó de Gall, Gafarró, Manto Negro,
Desconocida 1 and Viñaté).
6.- La combinación de las descripciones ampelográficas, los análisis genéticos de marcadores
moleculares y las referencias históricas han mostrado ser unas herramientas excelentes para
realizar una buena identificación de las variedades y para establecer una correcta viticultura
regional.
7.- Treinta y tres de las 159 variedades estudiadas han mostrado una alta resistencia al oídio
(Erysiphe necator) tanto en racimo como en hoja, siendo 10 de estas variedades procedentes
de las Islas Baleares. De las variedades estudiadas procedentes de las Islas Baleares,
Conclusiones
- 142 -
solamente la variedad Beba blanca mostró alta susceptibilidad tanto en racimo como en hoja a
esta enfermedad.
8.- La variedad, color de la baya y adaptaciones locales de la variedad a condiciones ambientales
juegan un importante papel en la rutas metabólicas para obtener compuestos aromáticos. Estas
rutas metabólicas parecen tener un componente genético.
9.- Teniendo en cuenta los resultados obtenidos en este estudio respecto a la relación entre
cultivar y territorio (referencias históricas y análisis microsatélites), comportamiento agronómico
(caracterización agronómica y resistencia al oídio), y potencial enológico de las variedades
estudiadas (caracterización aromática de uvas, caracterización química y sensorial de los vinos
realizados y preferencia de los catadores), se podrían recomendar siete variedades para
mejorar la calidad de los vinos y la conservación del patrimonio vitícola de las Islas Baleares.
Estas variedades, en orden de preferencia, son: para vinificaciones en blanco Argamusa y
Quigat, para vinificaciones en tinto Gorgollassa, Pampolat girat, Mandón y Excursach, y para
vinificaciones en rosado Mansés de Tibbus.
Appendix I: Plant material
Appendix I
- 143 -
Table 1. Plant material analyzed in this study from the Balearic Islands (except * from Levante area, ** from
Girona, *** from Tarragona) located at the Vitis Germplasm Bank “Finca El Encín” (IMIDRA, Alcalá de
Henares, Spain). Local names=names of the accession; Acc.: accession. Variables: Historical references;
Ampelographic description; Genetic analysis; Resistance to powdery mildew (Erysiphe necator Schwein.;
Resist. powd. mildew); Agronomic characterization (Agron. charact.); Must variables (Must var.); Grape
aroma characterization (Grape aroma charac.) and Sensorial wine analysis (Sensor. wine analysis).
Accession analyzed ()
Local name Acc. Berry color
Prime name at VGB “Finca El
Encín”
Historical Reference
Ampelographic description
1
Genetic analysis
Argamusa E34 white Argamusa no
Calop E32 white Beba
Calop blanco E43 white Beba
Corazón de Ángel * O16 white Beba
Grumiere blanco * O44 white Beba
Jaumes E14 white Beba
Massacamps E38 white Quigat
Mateu ** E39 white Beba
Pensal blanco, Moll, Prensal blanco
SCL white Pensal Blanca
Quigat E33 white Quigat
Valenci blanco * O17 white Beba
Viñaté E45 white Viñaté no
Calop rojo, Calop roig E19 rose Beba roja
Batista E23 black Batista
Beba negra, Calop negro, Calop negre
E18 black Valenci Tinto
Boal E21 black Bobal
Cabellis E20 black Manto Negro
Callet E26 black Callet
Eperó de Gall, Esperó de gall
E37 black Eperó de gall
Excursach, Escursach E27 black Excursach
Fernandella E22 black Fernandella no
Fogoneu E24 black Fogoneu
Fogoneu francés E16 black Fogoneu no
Galmete E45 black Mandón no
Giró E36 black Mancés de Capdell
Gorgollasa, Gorgollassa
E25 black Gorgollassa
Mancés de Capdell E31 black Mancés de Capdell
Mansés de tibbus, Mancés de Tibbus
E30 black Mansés de Tibbus
Manto negro E29 black Manto Negro
Pampolat Girat*** H24 black Pampolat girat
Sabaté, Sabater E35 black Sabaté
Vinaté, Vinater E11 black Bobal
Appendix I
- 144 -
Local name Acc. Berry color
Prime name at VGB
“Finca El Encín”
Resist. powd.
mildew2
Agron. charact.
3
Must var.
Grape aroma charac.
Sensor. wine
analysis4
Argamusa E34 white Argamusa no no
Calop E32 white Beba
Calop blanco E43 white Beba
Corazón de Ángel * O16 white Beba
Grumiere blanco * O44 white Beba
Jaumes E14 white Beba
Massacamps E38 white Quigat
Mateu ** E39 white Beba
Pensal blanco, Moll, Prensal blanco
SCL white Pensal Blanca
no
Quigat E33 white Quigat
Valenci blanco * O17 white Beba
Viñaté E45 white Viñaté no no
Calop rojo, Calop roig E19 rose Beba roja
Batista E23 black Batista
Beba negra, Calop negro, Calop negre
E18 black Valenci Tinto
Boal E21 black Bobal
Cabellis E20 black Manto Negro
Callet E26 black Callet
Eperó de Gall, Esperó de gall
E37 black Eperó de gall
Excursach, Escursach E27 black Excursach
Fernandella E22 black Fernandella no no
Fogoneu E24 black Fogoneu
Fogoneu francés E16 black Fogoneu
Galmete E45 black Mandón no
Giró E36 black Mancés de Capdell
Gorgollasa, Gorgollassa
E25 black Gorgollassa
Mancés de Capdell E31 black Mancés de Capdell
Mansés de tibbus, Mancés de Tibbus
E30 black Mansés de Tibbus
Manto negro E29 black Manto Negro
Pampolat Girat*** H24 black Pampolat girat
Sabaté, Sabater E35 black Sabaté
Vinaté, Vinater E11 black Bobal
1 Ampelographic description: the varieties not described were located in a different plot where the main analysis was carried out
2 Resistance to powdery mildew (Erysiphe necator Schwein.): Pensal Blanca was not analyzed since it was located in a different plot where the analysis was carried out
3 Agronomic characterization: the varieties were not analysed since they were located in a different plot where the main analysis was carried out
4 Sensory wine analysis: vinifications were made from all the accessions of the same variety when enough grape quantity was available
Appendix II: Ampelographic descriptions
and agronomic characterization
Appendix II
- 145 -
Ampelographic descriptions
Table 1. Ampelographic description per cultivar following OIV (1984) modified by Genres 081
(www.genres.de/vitis/vitis.htm). Modal data of 2006 and 2007 descriptions
Variety
Young shoot Young leaf and shoot Woody shoot
Flower
OIV-001
OIV-002
OIV-003
OIV-004
OIV-007
OIV-008
OIV-015-2
OIV-016
OIV-051
OIV-053
OIV-103
OIV-151
Batista 5 2 5 5 2 2 3 1 3 5 3 3
Beba blanca 5 3 3 4 2 2 2 1 1 5 2 3
Valenci Tinto 5 2 7 3 2 2 1 1 4 1 2 3
Beba roja 5 3 5 4 2 2 1 1 3 4 2 3
Bobal 5 2 3 7 2 2 2 1 3 7 2 3
Callet 5 1 1 1 2 2 1 1 3 1 2 3
Eperó de Gall 5 3 7 5 2 2 1 1 3 6 3 3
Excursach 5 1 1 1 1 2 1 1 3 1 2 4
Fogoneu 5 2 6 1 3 2 5 1 4 1 2 3
Mansés de Capdell (Giró)
5 2 3 7 2 2 3 1 3 6 2 3
Gorgollasa 5 3 3 7 1 2 1 1 3 7 1 3
Mansés de Tibbus 5 2 7 7 2 2 2 1 4 7 2 3
Manto Negro 5 1 1 1 2 2 1 1 3 1 2 3
Pampolat girat 5 3 4 5 1 1 1 1 3 5 2 3
Pensal Blanca 5 2 3 5 1 1 1 1 2 5 1 3
Quigat 5 3 3 4 2 2 1 1 1 4 2 3
Sabaté 5 1 1 7 2 2 1 1 3 6 2 3
Variety
Mature leaf
OIV-067
OIV-068
OIV-070
OIV-072
OIV-074
OIV-075
OIV-076
OIV-079
OIV-080
OIV-081-1
OIV-081-2
OIV-082
OIV-083-1
Batista 3 4 2 1 3 3 4 4 2 1 1 2 1
Beba blanca 3 3 1 2 5 5 3 5 3 2 1 3 1
Valenci Tinto 2 3 1 1 5 2 2 2 2 2 1 3 2
Beba roja 4 3 1 2 5 5 3 5 3 2 1 3 2
Bobal 4 3 1 2 5 5 3 5 2 1 1 2 2
Callet 3 3 1 1 5 4 3 3 2 1 1 3 2
Eperó de Gall 2 3 3 2 5 3 3 2 2 2 1 3 2
Excursach 2 3 1 1 5 3 4 3 2 1 1 3 3
Fogoneu 2 3 4 1 5 2 4 2 2 1 1 2 2 Mansés de Capdell (Giró)
3 3 2 1 5 3 3 2 2 1 1 2 2
Gorgollasa 2 3 1 2 5 3 2 3 2 1 1 1 3
Mansés de Tibbus 2 3 4 1 5 5 3 2 1 1 1 3 2
Manto Negro 2 3 1 1 5 3 3 2 1 2 1 3 2
Pampolat girat 3 3 1 2 3 5 2 3 2 2 1 2 2
Pensal Blanca 3 3 1 2 5 3 2 3 2 2 1 3 2
Quigat 3 3 1 1 5 3 3 3 2 2 1 3 2
Sabaté 2 3 3 1 5 5 3 2 1 1 2 3 2
Appendix II
- 146 -
Variety
Mature leaf Bunch
OIV-083-2
OIV-084
OIV-087
OIV-094
OIV-306
OIV-202
OIV-203
OIV-204
OIV-206
OIV-208
OIV-209
Batista 2 4 1 7 5 2 2 7 1 1 1
Beba blanca 2 5 3 5 1 6 4 4 3 2 2
Valenci Tinto 1 1 1 3 2 6 4 3 3 2 2
Beba roja 2 3 1 5 2 6 4 3 4 2 1
Bobal 2 8 1 5 4 4 4 5 1 2 2
Callet 1 1 1 3 2 5 4 5 2 2 2
Eperó de Gall 1 7 1 5 2 5 4 7 3 2 3
Excursach 1 1 1 5 2 5 4 7 2 2 3
Fogoneu 1 1 1 5 3 5 4 6 3 2 2
Mansés de Capdell (Giró) 1 4 1 5 1 4 4 7 2 3 2
Gorgollasa 1 7 1 3 2 5 3 4 2 2 2
Mansés de Tibbus 1 7 3 5 1 6 5 5 2 2 3
Manto Negro 2 1 1 3 1 7 4 5 3 2 2
Pampolat girat 2 7 1 7 4 4 4 5 2 2 3
Pensal Blanca 2 3 1 3 1 7 5 5 4 2 2
Quigat 2 5 3 5 1 5 4 5 1 2 2
Sabaté 2 6 1 5 2 6 5 7 1 2 3
Variety
Berry Phenology
OIV-220
OIV-221
OIV-223
OIV-225
OIV-230
OIV-231
OIV-232
OIV-235
OIV-236
OIV-241
OIV-301
OIV-303
OIV-304
Batista 5 5 2 5 1 1 3 1 1 3 5 3 5
Beba blanca 9 9 3 1 1 1 2 2 1 3 5 5 5
Valenci Tinto 9 7 3 5 1 1 2 2 1 3 5 5 5
Beba roja 9 7 3 2 1 1 2 2 1 3 5 7 5
Bobal 7 7 2 6 1 1 3 1 1 3 5 5 5
Callet 7 7 2 5 1 1 2 2 1 3 5 5 5
Eperó de Gall 7 7 2 5 1 1 3 1 5 3 7 7 5
Excursach 5 5 2 6 1 1 3 1 1 3 5 5 5
Fogoneu 7 7 2 6 1 1 3 1 1 3 5 5 5
Mansés de Capdell (Giró)
7 7 2 5 1 1 3 1 1 3 5 7 7
Gorgollasa 7 7 2 6 1 1 3 1 1 3 5 7 5
Mansés de Tibbus 7 7 3 5 1 1 3 1 1 3 7 7 5
Manto Negro 7 7 2 5 1 1 3 2 1 3 5 5 5
Pampolat girat 5 5 2 5 1 1 3 1 1 3 5 7 5
Pensal Blanca 7 7 2 1 1 1 3 1 1 3 5 7 7
Quigat 7 7 2 1 1 1 3 1 1 3 7 7 7
Sabaté 5 5 2 5 1 1 3 1 1 3 5 7 5
Agronomic descriptions
The following data corresponding to an agronomic characterization following OIV (1984) modified
by Genres 081 (www.genres.de/vitis/vitis.htm). Data media of 2006, 2007 and 2008 vintages.
References
OIV (Office International de la Vigne et du Vin) (1984) Codes des caractères descriptifs des
variétés et espèces de Vitis. Paris, France: Ed. Dedon.
Appendix II
- 147 -
Nombre principal: Batista; Accesión: E23
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 1,480±1,833
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,62±2,04
Peso del sarmiento (g): 30,57±5,70
kg Madera de poda/Cepa: 0,269±0,105
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,89±0,10
Nº Racimos/Cepa: 8,46±1,66
Peso del racimo (g): 152,08±32,63
Nº Bayas/Racimo: 113,00±14,15
Peso la baya (g): 1,71±0,14
Kg uva/Cepa: 0,454±0,288
Caracterización del mosto:
Rto. Mosto (%): 34,37±7,78
pH: 3,55±0,23
Acidez total (g/l TH2): 5,49±0,14
%VOL: 10,91±1,29
Nota de cata:
Vino que presenta una capa media baja, de color fresa con reflejos grosellas y salmón. En nariz es un vino con intensidad media baja, destacan los aromas a frutas rojas y tonos herbáceos, con ligeros aromas cítricos y de fruta negra. En boca es un vino ligero con acidez media y bien equilibrado.
Appendix II
- 148 -
Nombre principal: Beba blanca; Accesiones: E14, E32, E39, E43, O16, O17, O44
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,53±1,37
Peso del sarmiento (g): 52,70±6,80
kg Madera de poda/Cepa: 0,437±0,104
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,75±0,08
Nº Racimos/Cepa: 6,66±1,67
Peso del racimo (g): 236,92±42,76
Nº Bayas/Racimo: 102,16±12,38
Peso la baya (g): 2,76±0,32
Kg uva/Cepa: 1,230±0,281
Caracterización del mosto:
Rto. Mosto (%): 28,28±7,56
pH: 3,63±0,14
Acidez total (g/l TH2): 4,95±0,70
%VOL: 12,72±0,07
Nota de cata:
Vino limpio y brillante de color amarillo pajizo con reflejos acerados. En nariz presenta una buena intensidad aromática. Es un vino fresco, destacan los aromas a frutas tropicales y fruta con hueso (melocotón y albaricoque). Presenta notas florales, cítricas, herbáceas y un ligero tono a pera y manzana. En boca es ligero con acidez media y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,492±0,233
Appendix II
- 149 -
Nombre principal: Beba roja; Accesión: E19
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,473±0,241
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 7,07±1,17
Peso del sarmiento (g): 53,82±5,98
kg Madera de poda/Cepa: 0,380±0,086
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,88±0,14
Nº Racimos/Cepa: 6,30±1,12
Peso del racimo (g): 294,33±72,33
Nº Bayas/Racimo: 128,50±21,07
Peso la baya (g): 3,02±0,20
Kg uva/Cepa: 1,002±0,166
Caracterización del mosto:
Rto. Mosto (%): 28,14±7,96
pH: 3,39±0,11
Acidez total (g/l TH2): 5,96±0,34
%VOL: 11,45±0,65
Nota de cata:
Vino limpio y brillante de color amarillo pajizo con reflejos acerados. En nariz presenta una buena intensidad aromática. Es un vino fresco, destacan los aromas a manzana, cítricos y especiados. Presenta notas a frutas tropicales, florales y ligeros tonos a melocotón y fruta
roja. En boca es un vino ligero con acidez media y bien equilibrado.
Appendix II
- 150 -
Nombre principal: Bobal; Accesiones: E11, E21
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 9,78±1,93
Peso del sarmiento (g): 26,87±7,37
kg Madera de poda/Cepa: 0,264±0,099
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,42±0,21
Nº Racimos/Cepa: 3,92±1,06
Peso del racimo (g): 203,27±61,04
Nº Bayas/Racimo: 116,44±27,50
Peso la baya (g): 2,41±0,57
Kg uva/Cepa: 0,608±0,341
Caracterización del mosto:
Rto. Mosto (%): 34,59±5,82
pH: 3,49±0,20
Acidez total (g/l TH2): 6,15±0,91
%VOL: 13,82±1,10
Nota de cata:
Vino que presenta una capa alta, bien cubierto, de color púrpura con reflejos granates. En nariz es un vino con buena intensidad aromática, destacan los aromas a fruta negra y florales. Presenta tonos animales, lácticos, fruta roja y ligeros aromas herbáceos y especiados. En boca es un vino bien estructurado, con acidez media y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,862±0,872
Appendix II
- 151 -
Nombre principal: Callet; Accesión: E26
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 9,38±2,43
Peso del sarmiento (g): 19,41±4,09
kg Madera de poda/Cepa: 0,187±0,080
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,89±0,29
Nº Racimos/Cepa: 8,17±2,11
Peso del racimo (g): 380,34±123,31
Nº Bayas/Racimo: 201,00±16,91
Peso la baya (g): 2,30±0,51
Kg uva/Cepa: 1,603±0,761
Caracterización del mosto:
Rto. Mosto (%): 32,53±6,58
pH: 3,64±0,12
Acidez total (g/l TH2): 5,31±0,32
%VOL: 13,07±0,36
Nota de cata:
Vino que presenta una capa media, de color rubí con reflejos granates. En nariz es un vino con buena intensidad aromática, destacan los aromas especiados, de fruta negra y roja, con tonos herbáceos. En boca es un vino de cuerpo medio, con acidez media y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,136±0,064
Appendix II
- 152 -
Nombre principal: Eperó de Gall; Accesión: E37
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,45±1,36
Peso del sarmiento (g): 51,89±2,97
kg Madera de poda/Cepa: 0,433±0,066
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,85±0,25
Nº Racimos/Cepa: 6,93±1,06
Peso del racimo (g): 319,66±47,43
Nº Bayas/Racimo: 225,11±72,14
Peso la baya (g): 1,93±0,49
Kg uva/Cepa: 0,851±0,312
Caracterización del mosto:
Rto. Mosto (%): 32,70±8,02
pH: 3,45±0,08
Acidez total (g/l TH2): 6,49±1,11
%VOL: 13,91±0,79
Nota de cata:
Vino que presenta una capa media alta, de color rubí con reflejos granates. En nariz es un vino con buena intensidad aromática, destacan los aromas especiados y herbáceos, que recuerdan al pimiento verde, con tonos de fruta negra y roja. En boca es un vino de cuerpo medio, con acidez media y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,712±0,526
Appendix II
- 153 -
Nombre principal: Excursach; Accesión: E27
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,55±1,83
Peso del sarmiento (g): 36,96±12,65
kg Madera de poda/Cepa: 0,329±0,150
Parámetros de producción de vid:
Nº Racimos/Pámpano: 1,03±0,30
Nº Racimos/Cepa: 8,46±0,40
Peso del racimo (g): 224,49±182,20
Nº Bayas/Racimo: 144,94±93,55
Peso la baya (g): 1,62±0,25
Kg uva/Cepa: 0,858±0,785
Caracterización del mosto:
Rto. Mosto (%): 25,25±1,59
pH: 3,43±0,14
Acidez total (g/l TH2): 6,06±2,10
%VOL: 13,77±0,81
Nota de cata:
Vino que presenta una capa alta, bien cubierto, de color púrpura con reflejos granates. En nariz es un vino con buena intensidad aromática, destacan los aromas a fruta negra, herbáceos y fruta roja, con tonos especiados, lácticos y florales. En boca es un vino bien estructurado, con acidez media y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 1,187±0,960
Appendix II
- 154 -
Nombre principal: Fogoneu; Accesiones: E16, E24
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 9,08±0,91
Peso del sarmiento (g): 28,62±3,55
kg Madera de poda/Cepa: 0,261±0,053
Parámetros de producción de vid:
Nº Racimos/Pámpano: 1,23±0,22
Nº Racimos/Cepa: 11,18±3,00
Peso del racimo (g): 269,39±29,25
Nº Bayas/Racimo: 154,33±8,21
Peso la baya (g): 2,21±0,27
Kg uva/Cepa: 1,574±0,806
Caracterización del mosto:
Rto. Mosto (%): 26,49±2,91
pH: 3,45±0,15
Acidez total (g/l TH2): 6,39±1,96
%VOL: 12,41±0,36
Nota de cata:
Vino que presenta una capa alta, bien cubierto, de color granate con reflejos púrpura. En nariz presenta buena intensidad aromática destacando los aromas a frutas rojas, especiados y fruta negra. Presenta aromas herbáceos, lácticos y tonos florales. En boca es un vino bien estructurado, con acidez media y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,323±0,346
Appendix II
- 155 -
Nombre principal: Gorgollassa; Accesión: E25
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,40±1,38
Peso del sarmiento (g): 40,81±5,36
kg Madera de poda/Cepa: 0,347±0,097
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,85±0,47
Nº Racimos/Cepa: 7,27±3,09
Peso del racimo (g): 152,25±4,48
Nº Bayas/Racimo: 97,33±29,78
Peso la baya (g): 1,92±0,58
Kg uva/Cepa: 0,595±0,461
Caracterización del mosto:
Rto. Mosto (%): 32,39±8,72
pH: 3,47±0,03
Acidez total (g/l TH2): 5,19±0,18
%VOL: 14,66±0,46
Nota de cata:
Vino que presenta una capa media alta, de color cereza con reflejos granates y rubí. En nariz es un vino con buena intensidad aromática, destacan los aromas florales, herbáceos y de fruta negra con tonos de frutas rojas y especiados. En boca es un vino de cuerpo medio, con acidez media y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,815±0,602
Appendix II
- 156 -
Nombre principal: Mancés de Capell (Giró); Accesiones: E31, E36
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,79±0,77
Peso del sarmiento (g): 43,47±18,40
kg Madera de poda/Cepa: 0,387±0,185
Parámetros de producción de vid:
Nº Racimos/Pámpano: 1,14±0,31
Nº Racimos/Cepa: 10,37±1,85
Peso del racimo (g): 276,85±38,78
Nº Bayas/Racimo: 185,89±22,47
Peso la baya (g): 1,99±0,38
Kg uva/Cepa: 1,918±0,538
Caracterización del mosto:
Rto. Mosto (%): 34,48±6,04
pH: 3,47±0,18
Acidez total (g/l TH2): 4,23±0,81
%VOL: 11,36±0,76
Nota de cata:
Vino limpio y brillante de color salmón con reflejos de piel de cebolla. En nariz presenta una buena intensidad aromática, destacan los aromas a fruta negra y regaliz. Presenta notas a fruta roja, frutas tropicales y herbáceos con tonos florales. En boca es un vino ligero con acidez media baja y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,230±0,136
Appendix II
- 157 -
Nombre principal: Mansés de Tibbus; Accesión: E30
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,62±0,93
Peso del sarmiento (g): 58,77±1,95
kg Madera de poda/Cepa: 0,504±0,063
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,92±0,25
Nº Racimos/Cepa: 7,97±1,81
Peso del racimo (g): 332,44±46,42
Nº Bayas/Racimo: 220,94±46,83
Peso la baya (g): 2,06±0,47
Kg uva/Cepa: 1,471±0,917
Caracterización del mosto:
Rto. Mosto (%): 32,84±4,50
pH: 3,60±0,16
Acidez total (g/l TH2): 4,56±0,25
%VOL: 14,13±0,74
Nota de cata:
Vino limpio y brillante de color salmón con reflejos rosados. En nariz presenta una buena intensidad aromática, destacan los aromas a fruta con hueso y fruta negra. Presenta notas a fruta roja y frutas tropicales con tonos florales y herbáceos. En boca es un vino ligero con acidez media baja y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,455±0,240
Appendix II
- 158 -
Nombre principal: Manto Negro; Accesión: E20, E29
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,62±1,46
Peso del sarmiento (g): 33,73±7,68
kg Madera de poda/Cepa: 0,294±0,094
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,99±0,25
Nº Racimos/Cepa: 8,78±3,03
Peso del racimo (g): 295,05±56,69
Nº Bayas/Racimo: 175,78±7,92
Peso la baya (g): 1,94±0,32
Kg uva/Cepa: 1,295±0,348
Caracterización del mosto:
Rto. Mosto (%): 27,65±8,72
pH: 3,57±0,14
Acidez total (g/l TH2): 5,15±0,80
%VOL: 14,50±0,56
Nota de cata:
Vino que presenta una capa media, de color rubí con reflejos cerezas y grosellas. En nariz es un vino con buena intensidad aromática, destacan los aromas de fruta negra, florales y fruta roja, con tonos especiados y herbáceos. En boca es un vino de cuerpo medio, con acidez media y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,239±0,057
Appendix II
- 159 -
Nombre principal: Pampolat girat; Accesión: H24
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 9,38±2,19
Peso del sarmiento (g): 31,68±7,68
kg Madera de poda/Cepa: 0,293±0,084
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,89±0,46
Nº Racimos/Cepa: 8,14±3,30
Peso del racimo (g): 191,25±69,26
Nº Bayas/Racimo: 145,56±16,68
Peso la baya (g): 1,48±0,50
Kg uva/Cepa: 0,858±0,427
Caracterización del mosto:
Rto. Mosto (%): 31,67±3,44
pH: 3,47±0,26
Acidez total (g/l TH2): 5,16±1,13
%VOL: 13,63±0,81
Nota de cata:
Vino que presenta una capa media, de color cereza con reflejos rubí. En nariz es un vino de media intensidad aromática, destacan los aromas de fruta negra y roja, con tonos herbáceos y florales. En boca es un vino bien estructurado, con buena acidez y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,490±0,455
Appendix II
- 160 -
Nombre principal: Pensal Blanca; Accesión: SCL
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,93±2,30
Peso del sarmiento (g): 30,67±11,30
kg Madera de poda/Cepa: 0,274±0,143
Parámetros de producción de vid:
Nº Racimos/Pámpano: 1,05±0,79
Nº Racimos/Cepa: 8,35±3,42
Peso del racimo (g): 448,27±84,29
Nº Bayas/Racimo: 242,28±35,18
Peso la baya (g): 2,15±0,77
Kg uva/Cepa: 1,836±1,416
Caracterización del mosto:
Rto. Mosto (%): 36,37±7,51
pH: 3,56±0,15
Acidez total (g/l TH2): 3,63±0,78
%VOL: 12,91±1,44
Nota de cata:
Vino limpio y brillante de color amarillo pajizo con reflejos verdosos. En nariz presenta buena intensidad aromática. Es un vino fresco, destacan los aromas de melocotón, frutas tropicales (maracuyá, piña) y fruta verde como manzana y pera. Presenta aromas florales, herbáceas y especiadas. En boca es un vino ligero con acidez media y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,232±0,159
Appendix II
- 161 -
Nombre principal: Quigat; Accesiones: E33, E38
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,50±1,95
Peso del sarmiento (g): 22,39±1,08
kg Madera de poda/Cepa: 0,190±0,053
Parámetros de producción de vid:
Nº Racimos/Pámpano: 1,15±0,78
Nº Racimos/Cepa: 9,07±3,78
Peso del racimo (g): 361,75±57,75
Nº Bayas/Racimo: 198,06±42,84
Peso la baya (g): 2,33±0,66
Kg uva/Cepa: 1,617±1,002
Caracterización del mosto:
Rto. Mosto (%): 26,13±9,11
pH: 3,38±0,07
Acidez total (g/l TH2): 6,11±0,52
%VOL: 11,98±0,77
Nota de cata:
Vino limpio y brillante de color amarillo pajizo con reflejos verdosos. En nariz presenta una intensidad aromática media. Es un vino fresco, destacan los aromas a fruta verde, florales y herbáceos. Presenta notas a melocotón y frutas tropicales. En boca es un vino ligero con acidez media alta y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,222±0,171
Appendix II
- 162 -
Nombre principal: Sabaté; Accesión: E35
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 8,81±1,29
Peso del sarmiento (g): 25,66±4,13
kg Madera de poda/Cepa: 0,224±0,042
Parámetros de producción de vid:
Nº Racimos/Pámpano: 1,10±0,27
Nº Racimos/Cepa: 9,97±2,31
Peso del racimo (g): 366,97±120,27
Nº Bayas/Racimo: 310,06±115,98
Peso la baya (g): 1,47±0,41
Kg uva/Cepa: 1,918±1,109
Caracterización del mosto:
Rto. Mosto (%): 35,08±3,99
pH: 3,36±0,10
Acidez total (g/l TH2): 6,00±0,96
%VOL: 12,36±1,26
Nota de cata:
Vino limpio y brillante de color salmón con reflejos grosellas. En nariz presenta una buena intensidad aromática, destacan los aromas a frutas con hueso y regaliz. Presenta notas cítricas, a frutas negras, frutas tropicales y florales, con tonos de fruta roja. En boca es un vino ligero con acidez media y bien equilibrado.
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 0,150±0,090
Appendix II
- 163 -
Nombre principal: Valenci Tinto; Accesión: E18
Equilibrio Desarrollo/Producción:
Madera poda/Kg uva: 2,119±2,261
Parámetros de desarrollo de vid:
Número Sarmientos/Cepa: 7,78±0,79
Peso del sarmiento (g): 67,10±22,22
kg Madera de poda/Cepa: 0,536±0,218
Parámetros de producción de vid:
Nº Racimos/Pámpano: 0,62±0,28
Nº Racimos/Cepa: 5,39±1,92
Peso del racimo (g): 203,41±95,94
Nº Bayas/Racimo: 85,39±18,76
Peso la baya (g): 3,33±0,38
Kg uva/Cepa: 0,512±0,238
Caracterización del mosto:
Rto. Mosto (%): 32,21±7,01
pH: 3,87±0,03
Acidez total (g/l TH2): 4,03±0,38
%VOL: 14,22±0,44
Nota de cata:
Vino que presenta una capa media baja, de color cereza con reflejos rubí. En nariz es un vino con buena intensidad aromática, destacan los aromas a fruta roja y herbáceos con ligeros aromas a cítricos, fruta negra y tonos especiados. En boca es un vino de cuerpo medio y bien equilibrado.
Appendix II
OTRAS VARIEDADES ESTUDIADAS
Appendix II
- 167 -
Nombre principal: Argamusa; Accesión: E34
Parámetros de producción de vid:
Peso del racimo (g): 756,40±256,40
Nº Bayas/Racimo: 276,50±46,90
Peso la baya (g): 3,05±0,72
Caracterización del mosto:
Rto. Mosto (%): 27,09±2,26
pH: 3,52±0,16
Acidez total (g/l TH2): 3,98±1,36
%VOL: 11,14±0,17
Appendix II
- 168 -
Nombre principal: Fernandella; Accesión: E22
Parámetros de producción de vid:
Peso del racimo (g): 295,01±27,07
Nº Bayas/Racimo: 187,33±26,40
Peso la baya (g): 2,08±0,33
Caracterización del mosto:
Rto. Mosto (%):24,16±0,37
pH: 3,48±0,21
Acidez total (g/l TH2): 5,40±1,97
%VOL: 14,31±0,61
Appendix II
- 169 -
Nombre principal: Mandón; Accesión: E15
Parámetros de producción de vid:
Peso del racimo (g): 366,70±31,86
Nº Bayas/Racimo: 251,83±21,92
Peso la baya (g): 1,71±0,15
Caracterización del mosto:
Rto. Mosto (%):27,19±1,37
pH: 3,30±0,23
Acidez total (g/l TH2): 6,55±2,21
%VOL: 13,70±0,63
Nota de cata:
Vino que presenta una capa media, de color cereza con reflejos granates y rubí. En nariz es un vino con buena intensidad aromática, destacan los aromas de frutas rojas, lácticos y frutas negras, con tonos especiados y herbáceos. En boca es un vino de cuerpo medio, con acidez media y bien equilibrado.
Appendix II
- 170 -
Nombre principal: Viñaté; Accesión: E45
Parámetros de producción de vid:
Peso del racimo (g): 112,33±5,03
Nº Bayas/Racimo: 129,50±2,73
Peso la baya (g): 1,29±0,13
Caracterización del mosto:
Rto. Mosto (%): 32,55±0,72
pH: 3,77±0,16
Acidez total (g/l TH2): 4,63±0,34
%VOL: 13,73±1,53
Appendix III: Grape aroma data
Appendix III
- 171 -
Table 1. Compounds released by enzymatic hydrolysis from the glycosylated precursors extracted from red grapes (data are the average of the tree replicate samples; Keys in Chapter 6; Table 1). Concentrations (in micrograms per kilograms of grapes) and standard deviations. RT: Retention time; MQ (%): Match quality (%); nd: not detected
Compounds RT MQ (%) BAT.E23 BEN.E18 BOB.E11 BOB.E21 CAL.E26 EPE.E37 EXC.E27 FER.E22 FOG.E16 FOG.E24 GAL.E15 GIR.E31 GIR.E36 GOR.E25 MES.E30 MTN.E20 MTN.E29 PAM.H24 SAB.E35
1 hexanol 12.20 98 73.22 ±
14.62
67.51 ±
15.27
108.49 ±
19.78
136.71 ±
21.48
47.05 ±
7.63
100.21 ±
11.44
83.68 ±
6.51
91.38 ±
17.09 30.49 ± 9.45 29.11 ± 5.00
82.98 ±
10.78
27.01 ±
1.66
24.65 ±
2.70
91.01 ±
24.97 40.10 ± 4.60 50.11 ± 8.86 54.03 ± 8.42 66.29 ± 7.32
41.41 ±
5.94
2 cis-3-hexenol 13.53 98 17.22 ±
1.21 3.10 ± 0.61 4.37 ± 1.35 5.65 ± 1.31 2.01 ± 0.38 2.71 ± 0.56 4.78 ± 1.11
10.74 ±
1.84 5.67 ± 1.68 5.32 ± 1.13 2.90 ± 0.97 4.63 ± 0.39 4.13 ± 0.96 3.05 ± 0.4 2.12 ± 0.32 2.52 ± 0.13 3.36 ± 0.78 6.23 ± 1.73 4.21 ± 0.97
3 trans-2-hexenol 14.60 95 11.49 ±
3.65 9.53 ± 1.14 10.39 ± 1.41 16.34 ± 0.50 5.4 ± 0.56 6.28 ± 1.24
13.41 ± 1.23
10.75 ± 1.85
8.24 ± 1.20 6.63 ± 0.94 7.59 ± 0.55 4.64 ± 0.50 4.84 ± 0.64 5.6 ± 0.13 6.65 ± 1.78 8.25 ± 1.49 9.05 ± 0.99 13.77 ± 0.24 14.47 ±
1.31
4 tirosol 80.78 90 nd nd nd nd nd nd nd nd 14.26 ± 0.16 nd nd nd nd nd nd 21.16 ± 5.69 20.55 ± 6.95 nd 32.85 ±
6.87
alcohols 101.93 ±
18.37
80.14 ±
16.66
123.24 ±
22.54
158.70 ±
22.96
54.46 ±
8.43
109.21 ±
12.77
101.86 ±
5.15
112.87 ±
20.61 53.9 ± 13.75 41.06 ± 6.98
93.46 ±
11.69
36.28 ±
1.07
33.61 ±
3.84
99.66 ±
25.21 48.87 ± 6.68 82.03 ± 4.47
86.99 ±
13.29 86.30 ± 8.87
92.93 ±
2.36
5 benzaldehyde 20.11 95 2.59 ±
0.28 5.51 ± 2.53 4.10 ± 1.12 6.10 ± 0.82 4.67 ± 1.38 3.87 ± 0.56 5.78 ± 1.40 3.76 ± 0.32 2.10 ± 1.22 1.97 ± 0.57 3.84 ± 0.21 2.57 ± 0.68 2.02 ± 0.41 3.91 ± 1.01 2.37 ± 0.25 3.22 ± 0.64 3.9 ± 1.00 3.81 ± 0.90 5.53 ± 1.31
6 methyl salicylate 33.38 98 11.74 ±
3.35 269.71 ±
85.63 10.56 ± 1.97 13.98 ± 2.83
20.58 ± 6.37
4.69 ± 0.91 27.18 ± 21.48
6.56 ± 1.16 5.86 ± 0.01 nd 12.36 ±
2.92 6.03 ± 2.08 4.36 ± 1.38 10.93 ± 0.97 5.47 ± 0.81 5.69 ± 1.18 4.51 ± 1.39 12.55 ± 2.32
407.40 ± 2.24
7 α-methyl-benzenemethanol 36.01 98 4.51 ±
0.63 2.19 ± 0.58 3.79 ± 0.83 7.76 ± 0.77 3.53 ± 0.06 2.92 ± 0.55 4.62 ± 0.16 4.59 ± 0.28 4.24 ± 1.21 3.01 ± 0.81 3.70 ± 0.96 4.20 ± 0.36 4.41 ± 0.74 6.60 ± 1.94 5.59 ± 0.22 4.01 ± 0.52 5.37 ± 0.96 4.29 ± 0.81 5.44 ± 0.78
8 benzyl alcohol 39.32 98 238.39 ±
48.71
557.14 ±
64.86
474.65 ±
45.14
555.67 ±
15.92
562.21 ±
99.75
339.41 ±
45.04 368.24 ± 80
579.99 ±
40.52
148.97 ±
45.55
123.25 ±
18.41
343.90 ±
47.83
164.35 ±
37.63
133.21 ±
14.09
269.39 ±
39.20
271.47 ±
25.48
329.42 ±
83.2
363.96 ±
112.12
426.23 ±
65.64
1030.05 ±
128.92
9 2-phenylethanol 41.03 98 266.6 ±
22.46
143.35 ±
14.38
203.01 ±
24.86
215.42 ±
14.09
142.57 ±
8.20
150.55 ±
18.03
194.44 ±
10.33
273.27 ±
28.62
138.90 ±
28.29
125.32 ±
19.45
114.49 ±
27.26
184.66 ±
31.53
169.22 ±
2.42
160.9 ±
18.54
187.11 ±
5.16
164.66 ±
46.64
185.65 ±
63.32
233.98 ±
14.34
295.19 ±
12.19
10 eugenol 53.55 98 14.12 ±
3.9 7.29 ± 2.39 8.28 ± 1.72 8.77 ± 1.66 nd 6.73 ± 0.42 7.87 ± 1.27 8.66 ± 0.76 nd nd
10.42 ± 1.62
nd nd 14.89 ± 2.85 nd nd nd nd 9.05 ± 2.72
11 4-vinylguaicol 54.88 95 14.68 ±
4.19
12.63 ±
4.07 15.12 ± 7.79 14.21 ± 4.91
12.12 ±
2.37
10.03 ±
4.83
20.71 ±
6.67 8.70 ± 0.21 12.06 ± 2.10 9.68 ± 0.83
16.46 ±
3.72
11.79 ±
4.35 5.77 ± 0.14 23.65 ± 6.25 13.78 ± 2.74 10.79 ± 3.12 10.88 ± 2.68 13.10 ± 1.45
23.09 ±
6.26
12 4-vinylphenol 62.52 95 94.53 ±
46.68
28.71 ±
12.56
86.82 ±
23.87
45.61 ±
15.97
152.43 ±
7.39
68.93 ±
58.54
266.88 ±
139.27
28.85 ±
3.99
128.21 ±
5.05
153.11 ±
38.89
170.70 ±
48.55
61.33 ±
12.36
51.78 ±
10.16
67.34 ±
38.31
78.15 ±
48.39
41.78 ±
26.13
41.83 ±
13.29
91.13 ±
21.54
77.70 ±
21.12
13 methoxy eugenol 67.22 95 4.53 ±
1.43
14.08 ±
2.76 9.44 ± 2.69 6.73 ± 1.09 9.68 ± 1.42 9.02 ± 0.91
11.27 ±
2.14 5.83 ± 1.67 7.72 ± 2.26 7.81 ± 1.64
14.28 ±
2.52 nd nd 7.01 ± 0.98 5.90 ± 1.78 12.03 ± 2.89 9.76 ± 2.63 nd
28.16 ±
8.93
14 vanillin 67.63 90 19.01 ±
1.64 21.10 ±
4.51 25.11 ± 4.09 28.08 ± 8.45
21.43 ± 7.75
33.78 ± 10.01
30.35 ± 6.20
23.46 ± 3.26
15.23 ± 4.64 13.17 ± 2.95 27.13 ±
4.53 8.92 ± 1.56
11.35 ± 3.62
36.36 ± 9.56 25.99 ± 3.06 12.47 ± 3.67 15.45 ± 1.84 24.60 ± 6.25 16.46 ±
2.31
15 methyl vanillate 68.92 95 14.65 ±
0.73
31.72 ±
7.91
35.65 ±
12.62 41.51 ± 4.84
34.71 ±
11.00
18.95 ±
4.74
35.45 ±
5.18
18.68 ±
3.18 16.83 ± 4.03 10.91 ± 0.77
25.07 ±
7.70
18.65 ±
7.98
11.32 ±
2.32
32.72 ±
12.70 21.05 ± 5.42 11.45 ± 0.45 7.09 ± 2.27 16.94 ± 0.75
18.44 ±
3.63
16 zingerone 74.20 98 15.9 ±
3.20 44.56 ± 4.6
34.48 ±
10.79 30.17 ± 1.86
37.67 ±
3.23
57.59 ±
7.35
34.50 ±
2.85
16.49 ±
3.48 11.49 ± 2.11 9.43 ± 1.44
28.07 ±
4.19
12.98 ±
1.97
15.26 ±
3.53
51.44 ±
16.26 20.48 ± 2.46 21.77 ± 1.70 17.38 ± 2.59 25.46 ± 6.49
39.01 ±
1.52
17 homovanillyl alcohol 75.71 95 23.73 ±
1.31
27.45 ±
10.28 19.76 ± 3.92
60.67 ±
15.15
12.99 ±
3.12
41.96 ±
8.33 nd
25.89 ±
6.94 17.11 ± 2.91 23.21 ± 6.47
35.57 ±
10.11 nd
16.12 ±
2.76 20.71 ± 1.54 20.35 ± 0.01 21.05 ± 1.87 16.92 ± 3.73 79.55 ± 9.72
34.37 ±
5.83
18 syringaldehyde 78.54 95 nd nd 10.42 ± 1.54 16.19 ± 3.15 16.12 ±
0.01 nd nd nd nd nd nd nd nd 14.89 ± 1.47 nd nd 8.76 ± 1.72 22.34 ± 1.65
17.08 ± 0.01
19 methyl syringate 79.25 95 32.21 ±
1.03
25.63 ±
3.93
55.69 ±
19.48
53.23 ±
13.90
34.29 ±
7.42
26.98 ±
5.54
63.18 ±
3.83 nd
52.45 ±
21.85 34.24 ± 5.03
48.89 ±
16.44
39.96 ±
15.19
28.89 ±
3.17
42.06 ±
13.22 29.70 ± 5.22 21.01 ± 4.33 21.35 ± 8.55 65.85 ± 4.28
25.57 ±
5.96
20 dihydroconiferyl alcohol 79.71 95 77.89 ±
12.59
64.88 ±
0.52
87.72 ±
28.62 43.37 ± 3.68
39.54 ±
9.37
24.20 ±
7.54
40.09 ±
21.48
53.84 ±
17.29 19.67 ± 2.10 22.05 ± 8.88
24.57 ±
5.79
33.28 ±
10.18
24.24 ±
7.69 20.71 ± 3.60 25.91 ± 6.83
36.56 ±
10.52
57.16 ±
31.53 29.72 ± 2.83
29.87 ±
7.64
benzene compounds 835.09 ±
113.71
1255.94 ±
179.66
1074.51 ±
91.92
1147.46 ±
20.46
1093.82 ±
135.40
799.61 ±
152.94
1110.56 ±
238.16
1058.58 ±
89.53
576.93 ±
84.39
537.16 ±
48.60
879.47 ±
163.75
548.69 ±
112.36
477.94 ±
40.56
778.55 ±
136.63
699.77 ±
38.05
695.90 ±
135.41
769.98 ±
189.52
1049.56 ±
90.65
2051.00 ±
156.21
21 3,4-dihidro-3-oxo-a-ionol I 63.37 75 19.01 ±
3.22 19.13 ±
1.36 9.28 ± 1.78 13.76 ± 2.41
13.15 ± 3.59
11.17 ± 1.74
13.38 ± 2.39
13.49 ± 2.27
10.20 ± 0.84 9.96 ± 2.81 26.85 ±
5.52 17.79 ±
5.77 16.62 ±
3.65 23.93 ± 2.16 29.83 ± 2.84 25.06 ± 8.86 25.38 ± 7.44 24.07 ± 4.46
39.25 ± 6.95
22 3,4-dihidro-3-oxo-a-ionol II 64.38 75 30.89 ±
3.40
19.01 ±
1.87 10.36 ± 0.72 14.68 ± 0.76
15.31 ±
4.98
28.27 ±
3.52
25.21 ±
5.32
24.64 ±
3.80 18.43 ± 3.03 15.76 ± 4.10
26.16 ±
2.33
34.02 ±
8.42
31.95 ±
8.01 25.48 ± 4.16 25.62 ± 1.14 24.23 ± 4.27 21.06 ± 6.30 44.52 ± 8.53
31.46 ±
3.80
23 3,4-dihidro-3-oxo-a-ionol III 64.69 75 33.67 ±
4.78
20.40 ±
0.59 12.32 ± 0.20 16.04 ± 1.90
15.17 ±
3.48
29.49 ±
5.32
26.27 ±
4.38
23.14 ±
1.30 19.12 ± 2.21 16.30 ± 1.85
21.62 ±
3.50
30.59 ±
8.49
30.32 ±
8.29 25.11 ± 5.21 19.24 ± 1.94 22.83 ± 2.83 18.95 ± 4.48 41.68 ± 7.74
26.65 ±
1.58
24 3,4-dihidro-3-oxo-a-ionol IV 66.77 75 8.89 ±
1.98 8.44 ± 0.52 6.00 ± 1.42 10.38 ± 2.31 7.75 ± 2.42 8.92 ± 0.69 5.79 ± 1.18 4.37 ± 0.52 5.08 ± 0.82 4.02 ± 0.63
11.06 ±
2.85 4.84 ± 0.61 7.69 ± 2.14 16.27 ± 7.26 7.51 ± 1.26 8.95 ± 0.52 8.70 ± 2.73 7.68 ± 2.66
12.15 ±
0.82
25 3-hydroxy-β-damascone 66.96 95 24.34 ±
6.54 14.06 ±
2.59 8.98 ± 2.16 13.57 ± 2.49
13.72 ± 1.93
12.32 ± 2.23
26.46 ± 6.26
18.14 ± 5.78
10.94 ± 1.89 7.50 ± 0.74 22.43 ±
7.56 15.74 ±
1.79 32.68 ± 10.51
13.16 ± 3.10 12.67 ± 3.21 22.00 ± 6.78 22.67 ± 4.45 25.70 ± 0.59 18.11 ±
1.78
26 3-oxo-α-ionol 69.78 95 133.26 ±
24.41
139.42 ±
25.31
78.90 ±
16.86
87.32 ±
15.98
83.81 ±
2.27
48.78 ±
3.61
87.54 ±
9.91
107.89 ±
10.17 55.35 ± 6.99 51.39 ± 5.95
59.20 ±
17.14
158.00 ±
17.49
174.31 ±
11.75
165.64 ±
14.95
124.51 ±
13.91 87.91 ± 9.34
82.13 ±
24.52
234.07 ±
25.94
143.65 ±
5.05
27 4-oxo-α-ionol 69.98 90 22.18 ±
3.64
33.30 ±
10.77 12.70 ± 2.79 16.81 ± 4.29
14.40 ±
2.13
19.86 ±
1.22
22.57 ±
6.29
10.42 ±
2.14 16.48 ± 1.41 14.83 ± 1.41
20.33 ±
2.50
14.17 ±
1.93
16.04 ±
4.15 12.74 ± 3.54 9.74 ± 2.02 27.60 ± 4.66 24.75 ± 4.66 19.67 ± 4.15
13.48 ±
2.77
Appendix III
- 172 -
Compounds RT MQ (%) BAT.E23 BEN.E18 BOB.E11 BOB.E21 CAL.E26 EPE.E37 EXC.E27 FER.E22 FOG.E16 FOG.E24 GAL.E15 GIR.E31 GIR.E36 GOR.E25 MES.E30 MTN.E20 MTN.E29 PAM.H24 SAB.E35
28 3,9-dihydroxy-mega-5-ene 70.77 75 15.13 ±
3.25 19.90 ±
3.36 36.63 ± 3.10 35.15 ± 6.46
19.50 ± 5.03
20.79 ± 3.78
10.16 ± 0.01
17.69 ± 2.13
nd nd 34.91 ± 10.38
16.87 ± 1.70
25.37 ± 6.93
24.72 ± 6.51 14.64 ± 1.14 52.93 ± 7.22 52.02 ± 7.81 nd 28.88 ±
1.73
29 blumenol C 71.81 75 57.73 ±
9.83
18.24 ±
4.71 44.58 ± 4.18
60.35 ±
13.11 nd
21.20 ±
3.13
25.05 ±
5.99 25.9 ± 2.74 11.25 ± 3.16 10.66 ± 2.12
13.05 ±
3.73
17.08 ±
2.78
15.64 ±
2.78 25.88 ± 5.36 10.65 ± 2.59 25.91 ± 5.55
31.18 ±
10.31 nd 6.88 ± 1.81
30 5,6-epoxy-trans-β-ionone 72.38 75 7.87 ±
1.56
12.10 ±
3.69 9.03 ± 1.91 20.93 ± 4.91 8.48 ± 2.01
12.29 ±
3.24 9.08 ± 2.52 7.43 ± 1.17 2.89 ± 0.95 6.33 ± 1.89 nd 9.42 ± 1.86 9.22 ± 2.94 7.38 ± 1.78 9.02 ± 1.18 7.48 ± 1.09 3.39 ± 0.70 8.68 ± 2.13 nd
31 3-hydroxy-7,8-dihydro-β-
ionol 73.41 75
26.74 ±
6.82
31.00 ±
11.43 15.94 ± 5.10 17.93 ± 1.97
21.49 ±
4.05
17.03 ±
2.73 22.97 ± 4.9
35.49 ±
5.58 14.05 ± 1.94 24.25 ± 1.64
32.15 ±
7.37
24.71 ±
3.97
33.68 ±
4.41 27.34 ± 4.11 26.40 ± 2.81 36.14 ± 3.98 30.31 ± 6.14
42.00 ±
10.25
31.75 ±
1.98
32 vomifoliol 85.91 85 274.23 ±
20.16 274.49 ±
62.46 116.84 ±
20.98 163.37 ±
17.78 104.73 ±
22.40 112.57 ±
8.97 113.91 ±
23.42 174.06 ±
41.14 74.99 ± 24.61
75.12 ± 15.16
95.13 ± 25.12
230.06 ± 67
193.32 ± 37.59
97.91 ± 16.42
218.93 ± 21.56
141.14 ± 40.71
173.38 ± 43.15
204.00 ± 9.20
297.27 ± 42.80
norisoprenoids 653.93 ±
53.31
609.49 ±
107.45
361.55 ±
46.3
470.29 ±
27.43
317.52 ±
36.24
342.69 ±
14.52
381.62 ±
26.52
462.67 ±
55.91
238.78 ±
43.92
236.11 ±
10.34
362.9 ±
70.55
573.29 ±
111.18
586.84 ±
62.41
457.33 ±
34.27
508.76 ±
8.63
482.18 ±
54.69
493.91 ±
98.48
652.06 ±
64.38
647.25 ±
46.49
33 trans-furan linalool oxide 16.07 90 17.23 ±
3.72 8.50 ± 1.24 2.01 ± 0.70 1.17 ± 0.25 2.63 ± 0.44 7.94 ± 1.79
11.67 ±
1.01 8.76 ± 0.94 7.47 ± 0.80 5.62 ± 1.21 7.89 ± 1.57
19.40 ±
3.46
18.15 ±
2.10 16.58 ± 6.12 2.12 ± 0.31 5.47 ± 0.45 5.59 ± 0.90 8.34 ± 2.55 2.57 ± 0.34
34 cis-furan linalool oxide 17.51 90 18.06 ±
3.38 8.73 ± 1.59 2.32 ± 0.44 0.96 ± 0.14 3.29 ± 0.70 5.76 ± 0.68
16.22 ±
0.53
14.88 ±
1.55 7.75 ± 1.14 4.30 ± 0.26 8.14 ± 2.23 5.00 ± 1.10 7.77 ± 1.67 10.03 ± 5.68 4.10 ± 1.29 5.78 ± 1.77 6.95 ± 1.11 8.30 ± 1.12 4.79 ± 1.15
35 linalool 21.91 98 3.94 ± 0.01
nd 0.41 ± 0.17 0.63 ± 0.05 nd nd nd nd 2.76 ± 0.90 nd nd 29.68 ±
5.77 60.16 ±
7.54 nd nd 4.2 ± 0.01 4.14 ± 0.01 10.09 ± 2.19 nd
36 α-terpineol 29.65 98 16.55 ±
3.68
11.81 ±
2.33 nd nd 6.37 ± 1.33 nd 6.08 ± 1.11 2.20 ± 0.15 4.64 ± 1.29 6.41 ± 0.81 3.33 ± 1.06
24.81 ±
2.21 26.7 ± 2.86 45.19 ± 9.79 13.89 ± 2.50 5.08 ± 1.52 5.81 ± 0.82 31.33 ± 4.18 nd
37 trans-pyran linalool oxide 31.93 75 20.08 ±
3.20
13.15 ±
1.96 1.51 ± 0.18 nd 5.16 ± 0.73
17.10 ±
3.67
27.46 ±
2.83
19.43 ±
3.89 16.13 ± 3.30 10.28 ± 2.10
17.63 ±
4.32 8.93 ± 1.46
11.53 ±
1.73
32.21 ±
10.35 nd 8.12 ± 1.76 7.04 ± 2.19 11.77 ± 0.79 nd
38 cis-pyran linalool oxide 33.44 75 13.76 ±
3.57 3.63 ± 0.50 1.99 ± 0.08 nd 4.26 ± 1.36 3.05 ± 0.93 6.73 ± 0.23 9.32 ± 1.94 8.06 ± 0.69 4.22 ± 0.71 5.38 ± 1.47 3.94 ± 0.32 5.70 ± 1.21 5.75 ± 1.63 nd 2.80 ± 0.33 2.37 ± 0.71 6.04 ± 0.56 nd
39 citronellol 33.85 80 nd nd 1.42 ± 0.19 2.34 ± 0.61 3.47 ± 0.16 3.99 ± 1.09 4.78 ± 0.28 8.22 ± 2.63 5.13 ± 2.24 2.07 ± 0.01 nd 4.83 ± 0.87 2.24 ± 0.01 nd nd nd nd nd nd
40 nerol 35.51 98 42.82 ±
5.19 6.20 ± 1.55 10.16 ± 3.54 12.78 ± 1.29 9.34 ± 2.85
10.60 ±
1.21 8.98 ± 1.47
34.93 ±
4.45 5.80 ± 3.27 3.46 ± 0.35
10.29 ±
1.30 9.04 ± 3.20 8.39 ± 0.61 9.35 ± 2.50 12.94 ± 3.76 7.89 ± 2.44 9.40 ± 3.05 23.61 ± 5.13 6.59 ± 0.09
41 geraniol 38.18 98 108.6 ±
11.22
18.92 ±
4.24 25.99 ± 2.89 30.23 ± 1.83
20.07 ±
2.05
20.87 ±
4.84
19.09 ±
3.19 51.3 ± 3.09 17.59 ± 0.40 14.42 ± 2.41
12.21 ±
3.90
42.02 ±
4.39
54.87 ±
5.41 19.50 ± 5.84 58.54 ± 7.32 17.58 ± 4.71 18.71 ± 1.89
111.29 ±
13.04
15.68 ±
1.22
42 diol (2,6-dimetil-3,7-
ottadien-2,6-diolo) 43.52 95
13.58 ±
4.67 5.43 ± 0.57 nd nd nd 4.85 ± 0.95 6.46 ± 0.22 6.9 ± 1.27 nd nd 5.79 ± 1.64 9.92 ± 1.18 13.3 ± 2.99 9.47 ± 0.30 nd nd nd 5.17 ± 1.09 nd
43 endiol 45.17 75 5.54 ± 1.02
nd nd nd nd nd nd 4.04 ± 0.80 nd nd 3.20 ± 1.22 nd nd nd 3.97 ± 0.01 nd nd nd nd
44 cresol 46.04 90 nd nd nd 1.78 ± 0.05 1.70 ± 0.35 nd nd nd 1.50 ± 0.25 1.16 ± 0.35 2.86 ± 0.88 nd nd nd nd nd nd nd nd
45 hydroxy citronellol 55.89 75 nd nd nd nd 6.08 ± 1.62 9.39 ± 3.17 9.01 ± 0.37 6.21 ± 1.39 nd nd nd nd nd nd nd nd nd nd nd
46 8-hydroxydihydrolinalool 56.06 75 3.71 ±
0.88 9.31 ± 1.99 nd nd 4.32 ± 1.06 9.21 ± 2.58 3.86 ± 0.40 8.14 ± 1.30 3.49 ± 0.82 2.88 ± 0.22 4.15 ± 1.37
11.25 ±
3.22 7.4 ± 0.49 11.04 ± 3.43 nd 8.18 ± 1.57 8.57 ± 1.51 10.20 ± 0.74 9.98 ± 2.64
47 trans-8-hydroxy-linalool 58.52 90 51.39 ±
7.48 112.25 ±
9.98 9.47 ± 3.13 5.43 ± 1.03 6.85 ± 0.95
43.25 ± 3.85
18.42 ± 3.7 43.99 ±
5.08 19.68 ± 3.39 15.87 ± 0.20
14.89 ± 1.74
24.24 ± 6.27
26.52 ± 7.26
36.74 ± 1.61 9.03 ± 0.57 23.82 ± 6.68 31.78 ± 7.72 24.98 ± 4.43 29.45 ±
0.77
48 hydroxy geraniol 59.94 75 16.60 ±
12.13 3.88 ± 0.56 4.71 ± 3.60 2.85 ± 0.58 3.80 ± 0.70 6.06 ± 0.82 3.90 ± 0.81 7.71 ± 1.38 3.36 ± 0.96 3.58 ± 0.32 3.32 ± 1.18 19.7 ± 5.9
16.23 ±
3.41 5.33 ± 0.34 10.18 ± 1.19 3.61 ± 0.37 4.18 ± 0.76 9.14 ± 2.91 5.90 ± 1.05
49 cis-8-hydroxy-linalool 59.97 75 56.75 ±
17.08
42.60 ±
3.38 15.67 ± 2.33 16.31 ± 1.05
30.30 ±
4.11
71.07 ±
5.65
18.23 ±
3.20
48.08 ±
4.47 36.24 ± 8.72 30.37 ± 3.78
16.66 ±
2.03
245.92 ±
47.64
257.40 ±
50.66 46.45 ± 3.55 34.91 ± 9.78 22.65 ± 3.48 21.46 ± 2.53
71.99 ±
15.70
31.71 ±
3.41
50 geranic acid 60.67 95 54.19 ±
5.30
26.37 ±
4.05 13.80 ± 2.58 16.57 ± 3.82
21.44 ±
5.50
55.61 ±
2.02
36.71 ±
6.19
24.34 ±
2.01 17.48 ± 3.90 18.51 ± 4.38
49.48 ±
10.41
55.35 ±
11.68
69.55 ±
7.38 44.04 ± 2.21 26.67 ± 1.93 15.49 ± 2.33 17.55 ± 1.44
99.45 ±
12.08
13.27 ±
2.77
51 p-menthene-7,8-diol 66.67 95 125.76 ±
34.78 55.37 ±
2.29 nd 10.13 ± 2.03
14.69 ± 3.36
9.77 ± 1.74 nd nd 6.55 ± 0.72 11.27 ± 2.50 13.81 ±
4.04 45.63 ±
4.88 44.73 ± 12.62
26.29 ± 0.66 43.9 ± 3.46 22.61 ± 5.28 18.31 ± 5.31 98.97 ± 24.38
nd
terpenes 565.92 ±
111.15
326.15 ±
27.87
87.68 ±
11.53 95.52 ± 8.02
143.76 ±
8.66
275.26 ±
32.25
192.99 ±
4.53
296.40 ±
11.87
158.30 ±
14.87
133.03 ±
1.07
174.42 ±
19.48
559.66 ±
72.83
629.16 ±
74.58
317.99 ±
39.97
217.61 ±
9.36
150.47 ±
20.63
159.08 ±
10.95
530.67 ±
80.09
119.92 ±
3.64
Total 2156.88
± 244.83
2271.72 ±
271.62
1646.99 ±
124.62
1871.97 ±
71.61
1609.56 ±
169.03
1526.78 ±
182.73
1787.03 ±
222.15
1930.52 ±
176.07
1027.92 ±
145.08
947.35 ±
63.60
1510.24 ±
263.48
1717.93 ±
267.17
1727.56 ±
136.08
1653.53 ±
211.08
1475.01 ±
37.66
1410.58 ±
207.76
1509.96 ±
288.26
2318.58 ±
201.84
2911.11 ±
201.02
Appendix III
- 173 -
Table 2 Compounds released by enzymatic hydrolysis from the glycosylated precursors extracted from white and rose grapes (data are the average of the tree replicate samples; Keys in Chapter 6; Table 1). Concentrations (in micrograms per kilograms of grapes) and standard deviations. RT: Retention time; MQ (%): Match quality (%); nd: not detected
Compounds RT MQ (%) ARG.E34 BEB.E14 BEB.E32 BEB.E39 BEB.E43 BEB.O16 BEB.O17 BEB.O44 BER.E19 PEN.SC QUI.E33 QUI.E38 VIN.E45
1 hexanol 12.20 98 32.03 ± 2.52 31.18 ± 1.57 22.23 ± 0.38 34.06 ± 3.46 27.50 ± 3.47 29.59 ± 4.80 29.73 ± 4.45 44.86 ± 2.51 33.93 ± 4.56 27.79 ± 2.28 56.66 ± 4.13 49.96 ± 7.46 55.39 ± 6.82
2 cis-3-hexenol 13.53 98 6.99 ± 0.55 2.32 ± 0.57 2.03 ± 0.17 1.91 ± 0.29 2.60 ± 0.61 2.32 ± 0.34 2.39 ± 0.74 1.33 ± 0.15 3.60 ± 0.77 15.52 ± 1.14 1.99 ± 0.25 2.66 ± 0.56 9.42 ± 0.61
3 trans-2-hexenol 14.60 95 10.58 ± 1.77 6.14 ± 0.96 7.48 ± 1.12 3.96 ± 0.23 8.16 ± 0.68 12.61 ± 2.01 8.94 ± 1.06 5.03 ± 0.16 10.41 ± 2.70 14.90 ± 0.97 3.97 ± 0.76 3.03 ± 0.64 7.72 ± 0.06
4 tirosol 80.78 90 nd nd nd nd nd nd nd nd nd nd nd nd nd
alcohols 49.60 ± 2.70 39.64 ± 2.91 31.75 ± 1.46 39.93 ± 3.40 38.26 ± 4.65 44.52 ± 6.75 41.06 ± 6.25 51.22 ± 2.56 47.94 ± 3.51 58.20 ± 2.79 62.63 ± 4.85 55.65 ± 7.82 72.53 ± 7.24
5 benzaldehyde 20.11 95 1.99 ± 0.39 4.16 ± 1.66 2.29 ± 0.26 3.06 ± 1.06 2.10 ± 0.50 2.18 ± 0.68 5.39 ± 4.09 3.13 ± 0.88 nd 2.38 ± 0.20 2.26 ± 0.46 3.45 ± 0.93 140.66 ± 4.02
6 methyl salicylate 33.38 98 10.04 ± 3.08 17.56 ± 7.77 23.4 ± 6.79 nd 10.28 ± 6.47 19.96 ± 10.10 48.72 ± 36.94 4.02 ± 0.24 17.19 ± 6.77 4.46 ± 0.48 7.41 ± 1.63 7.57 ± 0.93 10.32 ± 1.50
7 α-methyl-benzenemethanol 36.01 98 3.15 ± 0.77 2.98 ± 0.47 3.57 ± 0.58 2.89 ± 0.38 3.63 ± 0.39 3.95 ± 0.25 3.30 ± 0.90 2.86 ± 0.82 4.89 ± 2.59 2.76 ± 0.29 3.23 ± 0.15 3.72 ± 0.45 7.03 ± 0.45
8 benzyl alcohol 39.32 98 180.07 ± 10.84 212.03 ± 29.01 291.17 ± 33.75 211.49 ± 20.76 122.86 ± 12.17 283.61 ± 27.78 237.99 ± 25.57 154.61 ± 24.17 191.18 ± 57.60 167.53 ± 5.99 298.64 ± 21.86 418.50 ± 77.16 536.78 ± 14.65
9 2-phenylethanol 41.03 98 187.23 ± 21.57 243.92 ± 27.30 268.58 ± 32.52 214.70 ± 14.09 296.23 ± 31.87 270.81 ± 30.99 245.23 ± 47.59 183.67 ± 12.85 257.77 ± 52.33 120.32 ± 5.53 258.82 ± 12.68 271.72 ± 4.63 306.42 ± 18.12
10 eugenol 53.55 98 5.55 ± 1.64 3.16 ± 0.52 6.30 ± 1.22 5.12 ± 1.65 2.69 ± 0.92 5.44 ± 1.06 6.51 ± 2.33 3.50 ± 0.85 5.19 ± 1.74 3.72 ± 0.14 6.08 ± 0.89 5.84 ± 1.19 15.97 ± 2.69
11 4-vinylguaicol 54.88 95 9.87 ± 1.85 10.51 ± 2.43 7.97 ± 0.34 9.34 ± 0.46 9.64 ± 1.59 11.73 ± 2.24 12.41 ± 4.11 11.68 ± 2.30 10.11 ± 0.33 16.75 ± 1.01 23.14 ± 4.52 22.61 ± 2.55 22.65 ± 3.25
12 4-vinylphenol 62.52 95 13.11 ± 2.42 18.73 ± 4.97 7.96 ± 1.33 7.56 ± 0.58 13.29 ± 0.85 11.84 ± 3.64 9.62 ± 2.79 17.66 ± 7.79 10.14 ± 1.57 4.79 ± 1.36 21.38 ± 4.67 29.31 ± 6.25 15.76 ± 1.37
13 methoxy eugenol 67.22 95 5.24 ± 1.12 4.58 ± 1.45 8.90 ± 1.81 4.28 ± 0.39 5.89 ± 1.35 7.66 ± 0.37 8.05 ± 0.92 3.14 ± 0.01 8.22 ± 3.44 nd 6.34 ± 1.28 2.99 ± 0.25 40.48 ± 2.50
14 vanillin 67.63 90 8.83 ± 0.98 10.02 ± 4.41 16.27 ± 5.09 24.19 ± 10.11 14.77 ± 4.46 13.73 ± 4.12 20.75 ± 6.53 11.77 ± 2.25 12.42 ± 1.58 16.18 ± 4.22 13.31 ± 2.20 17.70 ± 5.47 10.24 ± 1.25
15 methyl vanillate 68.92 95 nd nd 4.49 ± 0.01 nd nd nd nd nd nd 7.56 ± 1.95 11.50 ± 2.72 10.78 ± 1.79 8.90 ± 0.62
16 zingerone 74.20 98 18.09 ± 1.85 29.82 ± 0.79 15.62 ± 0.84 15.22 ± 1.53 25.81 ± 5.77 12.30 ± 3.46 16.95 ± 4.55 26.10 ± 2.56 27.49 ± 3.98 17.89 ± 1.16 17.17 ± 0.74 22.14 ± 0.24 42.10 ± 2.40
17 homovanillyl alcohol 75.71 95 35.03 ± 15.16 27.25 ± 8.66 12.74 ± 3.78 10.57 ± 0.92 31.98 ± 2.59 13.01 ± 2.78 15.21 ± 2.62 17.59 ± 2.62 32.92 ± 0.82 37.27 ± 3.22 46.30 ± 13.55 40.60 ± 11.56 35.66 ± 5.93
18 syringaldehyde 78.54 95 nd nd nd nd nd nd nd nd nd nd nd nd 19.21 ± 1.67
19 methyl syringate 79.25 95 nd nd nd nd nd nd nd nd nd nd 19.83 ± 3.83 17.87 ± 4.68 nd
20 dihydroconiferyl alcohol 79.71 95 56.75 ± 24.26 41.09 ± 6.92 46.77 ± 4.75 57.02 ± 18.43 76.05 ± 24.42 63.15 ± 18.95 48.99 ± 3.95 62.80 ± 27.99 49.66 ± 15.13 104.12 ± 13.26 49.31 ± 4.63 44.12 ± 7.07 30.09 ± 4.25
benzene compounds 534.97 ± 32.23 625.80 ± 70.23 713.03 ± 72.47 565.44 ± 49.83 614.33 ± 53.47 715.02 ± 10.75 679.10 ± 118.70 495.18 ± 69.21 627.19 ± 109.95 505.70 ± 14.70 784.73 ± 14.12 918.92 ± 103.31 1242.26 ± 12.48
21 3,4-dihidro-3-oxo-a-ionol I 63.37 75 19.36 ± 4.49 16.61 ± 1.47 20.46 ± 6.59 18.10 ± 2.33 21.85 ± 4.14 15.46 ± 2.49 22.76 ± 2.66 20.21 ± 5.67 19.62 ± 4.81 8.60 ± 1.59 14.52 ± 1.64 19.27 ± 3.59 20.03 ± 1.98
22 3,4-dihidro-3-oxo-a-ionol II 64.38 75 18.69 ± 3.76 18.91 ± 1.58 19.26 ± 6.30 15.67 ± 1.98 25.54 ± 2.45 14.05 ± 2.54 20.88 ± 5.10 23.26 ± 4.91 20.73 ± 2.18 7.36 ± 0.37 22.96 ± 1.33 31.36 ± 5.83 36.28 ± 5.65
23 3,4-dihidro-3-oxo-a-ionol III 64.69 75 17.78 ± 3.41 22.09 ± 1.26 19.74 ± 3.77 17.94 ± 1.34 29.82 ± 4.08 16.57 ± 2.50 23.92 ± 4.23 22.21 ± 2.80 25.06 ± 1.33 12.10 ± 0.34 22.62 ± 2.47 28.24 ± 3.76 36.55 ± 3.09
24 3,4-dihidro-3-oxo-a-ionol IV 66.77 75 7.16 ± 0.77 5.36 ± 0.26 6.00 ± 1.33 7.62 ± 0.55 13.84 ± 2.37 7.43 ± 1.34 10.79 ± 1.66 7.87 ± 1.09 12.80 ± 0.94 4.23 ± 0.78 6.31 ± 1.57 10.67 ± 3.18 11.64 ± 1.18
25 3-hydroxy-β-damascone 66.96 95 8.73 ± 1.67 12.31 ± 2.32 10.28 ± 0.85 10.90 ± 2.01 20.47 ± 6.09 10.66 ± 4.06 12.52 ± 1.58 10.75 ± 2.30 16.52 ± 4.68 nd 26.77 ± 3.74 24.55 ± 4.99 26.65 ± 2.96
26 3-oxo-α-ionol 69.78 95 78.72 ± 11.85 193.44 ± 13.43 209.96 ± 53.23 166.20 ± 16.75 192.96 ± 34.97 181.38 ± 32.53 183.69 ± 24.87 183.55 ± 26.26 171.19 ± 14.69 20.65 ± 2.15 127.11 ± 13.18 126.26 ± 5.98 123.62 ± 22.81
27 4-oxo-α-ionol 69.98 90 16.34 ± 2.39 21.19 ± 2.73 17.04 ± 3.40 13.98 ± 1.84 19.43 ± 3.72 17.25 ± 1.87 15.97 ± 0.22 18.00 ± 2.24 11.29 ± 3.42 7.00 ± 1.99 18.83 ± 3.17 16.44 ± 1.70 29.66 ± 9.69
Appendix III
- 174 -
Compounds RT MQ (%) ARG.E34 BEB.E14 BEB.E32 BEB.E39 BEB.E43 BEB.O16 BEB.O17 BEB.O44 BER.E19 PEN.SC QUI.E33 QUI.E38 VIN.E45
28 3,9-dihydroxy-mega-5-ene 70.77 75 22.47 ± 3.97 35.49 ± 0.65 17.39 ± 4.44 23.76 ± 0.59 40.17 ± 6.22 12.67 ± 1.02 28.20 ± 1.15 39.12 ± 5.12 32.01 ± 6.26 6.94 ± 1.26 49.66 ± 6.59 42.34 ± 1.54 55.20 ± 11.77
29 blumenol C 71.81 75 7.44 ± 1.08 14.20 ± 1.26 12.73 ± 2.57 10.07 ± 1.97 25.91 ± 4.73 9.21 ± 2.21 15.52 ± 3.96 20.04 ± 4.87 18.30 ± 2.81 10.98 ± 2.13 42.26 ± 4.14 39.34 ± 0.97 36.23 ± 6.29
30 5,6-epoxy-trans-β-ionone 72.38 75 2.65 ± 0.52 6.84 ± 1.30 6.16 ± 1.32 7.84 ± 2.25 6.77 ± 1.25 6.68 ± 1.45 9.45 ± 1.99 7.92 ± 2.14 10.62 ± 2.71 nd 9.02 ± 2.09 10.92 ± 4.86 33.21 ± 5.32
31 3-hydroxy-7,8-dihydro-β-ionol 73.41 75 14.76 ± 0.49 12.32 ± 2.28 19.53 ± 3.99 19.04 ± 5.76 33.13 ± 7.78 23.74 ± 7.54 21.05 ± 3.17 20.01 ± 1.64 25.37 ± 7.93 8.63 ± 1.06 36.07 ± 3.72 33.69 ± 3.10 42.52 ± 8.11
32 vomifoliol 85.91 85 170.03 ± 51.81 165.84 ± 21.62 188.33 ± 32.33 151.17 ± 48.86 296.68 ± 44.60 234.41 ± 26.30 240.48 ± 71.05 159.13 ± 25.99 268.38 ± 55.08 269.42 ± 12.16 279.07 ± 39.09 234.18 ± 40.54 249.79 ± 24.99
norisoprenoids 384.14 ± 74.68 524.61 ± 37.54 546.88 ± 101.27 462.28 ± 57.33 726.57 ± 73.53 549.50 ± 54.47 605.24 ± 109.4 532.07 ± 54.36 631.89 ± 64.91 355.92 ± 10.53 655.21 ± 68.96 617.25 ± 28.75 701.38 ± 74.48
33 trans-furan linalool oxide 16.07 90 5.45 ± 0.50 12.25 ± 1.53 13.38 ± 3.82 10.25 ± 0.66 12.16 ± 1.72 13.08 ± 2.50 7.72 ± 1.94 8.30 ± 1.28 16.21 ± 2.59 2.06 ± 0.10 nd nd nd
34 cis-furan linalool oxide 17.51 90 3.61 ± 0.67 6.64 ± 1.24 9.61 ± 2.21 10.14 ± 0.81 14.54 ± 2.97 13.52 ± 4.24 6.92 ± 1.28 6.71 ± 0.59 11.49 ± 3.81 6.32 ± 0.35 nd nd nd
35 linalool 21.91 98 nd 15.83 ± 4.88 7.65 ± 1.52 6.85 ± 2.16 nd 4.39 ± 0.96 9.88 ± 3.44 5.98 ± 1.35 9.94 ± 2.32 nd nd nd nd
36 α-terpineol 29.65 98 nd 8.00 ± 2.49 9.97 ± 4.59 4.53 ± 0.39 8.65 ± 1.03 8.61 ± 1.28 5.31 ± 0.32 3.88 ± 0.40 7.21 ± 1.41 2.54 ± 0.28 3.61 ± 1.16 2.07 ± 0.10 nd
37 trans-pyran linalool oxide 31.93 75 6.24 ± 1.39 19.09 ± 2.45 24.05 ± 6.10 15.89 ± 2.46 23.20 ± 2.54 19.50 ± 3.24 14.05 ± 3.51 10.48 ± 1.34 21.76 ± 2.70 2.93 ± 0.62 nd nd nd
38 cis-pyran linalool oxide 33.44 75 2.23 ± 0.65 3.84 ± 0.42 5.45 ± 0.49 4.63 ± 0.78 10.38 ± 1.38 7.90 ± 2.49 4.12 ± 1.20 3.41 ± 0.48 5.82 ± 1.86 0.62 ± 0.14 nd nd nd
39 citronellol 33.85 80 2.85 ± 0.59 nd nd nd nd nd nd 5.77 ± 1.72 nd nd 3.13 ± 0.01 4.66 ± 0.01 nd
40 nerol 35.51 98 10.23 ± 1.23 19.27 ± 4.22 12.27 ± 3.93 9.36 ± 0.38 6.44 ± 1.64 10.32 ± 2.59 12.1 ± 2.86 12.46 ± 2.33 12.47 ± 3.09 5.61 ± 0.84 5.96 ± 1.13 5.85 ± 2.40 10.69 ± 3.05
41 geraniol 38.18 98 47.99 ± 0.66 35.40 ± 1.12 30.05 ± 6.68 24.56 ± 1.78 21.86 ± 2.30 22.53 ± 1.37 20.72 ± 4.31 29.27 ± 2.96 25.99 ± 4.73 11.49 ± 0.40 13.26 ± 2.21 6.71 ± 0.56 28.64 ± 1.75
42 diol (2,6-dimetil-3,7-ottadien-2,6-diolo) 43.52 95 3.48 ± 0.70 7.92 ± 1.81 8.80 ± 2.20 10.71 ± 0.23 6.79 ± 1.67 9.61 ± 2.20 9.6 ± 1.33 9.97 ± 0.80 6.67 ± 1.38 8.84 ± 0.17 nd nd nd
43 endiol 45.17 75 6.01 ± 0.57 nd 5.75 ± 2.04 4.87 ± 0.15 3.52 ± 0.52 nd nd 5.48 ± 0.84 nd 2.20 ± 0.68 nd nd nd
44 cresol 46.04 90 nd nd nd nd 2.66 ± 0.73 2.63 ± 0.65 nd nd nd nd nd nd 4.14 ± 1.08
45 hydroxy citronellol 55.89 75 12.64 ± 2.93 nd nd nd nd nd nd nd nd 5.94 ± 1.66 7.59 ± 0.41 8.06 ± 0.15 nd
46 8-hydroxydihydrolinalool 56.06 75 nd 8.97 ± 1.10 5.42 ± 0.52 7.25 ± 1.28 4.49 ± 0.96 5.91 ± 1.91 6.88 ± 0.91 11.40 ± 1.78 6.84 ± 1.72 3.65 ± 0.86 4.27 ± 0.52 5.41 ± 0.51 10.09 ± 2.46
47 trans-8-hydroxy-linalool 58.52 90 14.80 ± 3.25 38.50 ± 3.26 43.22 ± 7.45 43.42 ± 3.89 50.32 ± 9.13 34.41 ± 3.29 41.41 ± 4.23 38.55 ± 4.68 36.06 ± 3.31 23.49 ± 1.66 9.17 ± 1.31 7.55 ± 0.97 11.67 ± 1.13
48 hydroxy geraniol 59.94 75 30.34 ± 3.02 24.96 ± 3.86 16.52 ± 8.59 7.53 ± 0.94 5.57 ± 1.77 6.54 ± 0.70 12.21 ± 4.69 12.59 ± 3.06 8.66 ± 1.56 3.33 ± 0.19 4.38 ± 0.58 3.08 ± 0.31 12.25 ± 0.28
49 cis-8-hydroxy-linalool 59.97 75 39.96 ± 9.33 150.78 ± 10.54 102.16 ± 30.58 99.15 ± 5.19 60.72 ± 7.30 77.26 ± 11.40 107.68 ± 8.12 134.81 ± 9.41 124.74 ± 25.22 16.57 ± 0.57 22.76 ± 5.53 21.26 ± 6.81 68.41 ± 5.19
50 geranic acid 60.67 95 34.28 ± 1.76 71.47 ± 6.69 58.86 ± 9.82 55.39 ± 10.06 59.71 ± 4.64 47.51 ± 7.44 58.81 ± 8.12 80.35 ± 13.76 48.53 ± 8.93 32.20 ± 1.35 21.87 ± 2.78 22.71 ± 3.99 11.47 ± 1.11
51 p-menthene-7,8-diol 66.67 95 nd 12.23 ± 1.72 17.73 ± 7.32 9.52 ± 1.14 20.52 ± 8.59 17.01 ± 2.52 8.80 ± 2.02 12.11 ± 0.94 16.78 ± 4.47 5.85 ± 0.96 20.69 ± 6.79 15.20 ± 4.09 nd
terpenes 218.95 ± 17.90 435.13 ± 10.62 370.89 ± 90.70 324.04 ± 22.28 311.54 ± 30.43 300.72 ± 27.80 326.22 ± 20.15 391.52 ± 34.79 359.17 ± 38.01 133.64 ± 4.67 114.61 ± 10.86 99.45 ± 5.76 157.36 ± 8.28
Total 1187.66 ± 110.49 1625.17 ± 108.85 1662.55 ± 245.96 1391.69 ± 92.45 1690.70 ± 123.8 1609.76 ± 59.89 1651.62 ± 239.48 1469.99 ± 33.70 1666.19 ± 142.24 1053.47 ± 8.59 1617.18 ± 93.3 1691.27 ± 127.71 2173.53 ± 84.86
Appendix III
- 175 -
Table 3. Odour active value (OAV) and standard deviations in red varieties. Only compounds with OAV > 0.20 are shown (data are the average of the tree replicate samples; Keys in Chapter 6; Table 1). RT: Retention time; MQ (%): Match quality (%); nd: not detected
Compounds RT MQ (%) BAT.E23 BEN.E18 BOB.E11 BOB.E21 CAL.E26 EPE.E37 EXC.E27 FER.E22 FOG.E16 FOG.E24
5 benzaldehyde 20.11 95 0.01 ± 0.00 0.02 ± 0.01 0.01 ± 0.00 0.02 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00
8 benzyl alcohol 39.32 98 0.25 ± 0.05 0.83 ± 0.13 0.69 ± 0.06 0.67 ± 0.07 0.87 ± 0.18 0.44 ± 0.07 0.38 ± 0.06 0.68 ± 0.03 0.22 ± 0.05 0.19 ± 0.03
9 2-phenylethanol 41.03 98 0.12 ± 0.01 0.09 ± 0.00 0.12 ± 0.01 0.11 ± 0.00 0.09 ± 0.00 0.08 ± 0.01 0.08 ± 0.01 0.13 ± 0.01 0.08 ± 0.01 0.08 ± 0.01
10 eugenol 53.55 98 1.87 ± 0.52 1.30 ± 0.29 1.49 ± 0.27 1.30 ± 0.18 nd 1.07 ± 0.05 1.02 ± 0.08 1.26 ± 0.12 nd nd
11 4-vinylguaicol 54.88 95 0.24 ± 0.06 0.28 ± 0.08 0.34 ± 0.16 0.26 ± 0.08 0.29 ± 0.07 0.20 ± 0.10 0.35 ± 0.14 0.16 ± 0.00 0.28 ± 0.05 0.23 ± 0.02
12 4-vinylphenol 62.52 95 0.34 ± 0.16 0.14 ± 0.05 0.44 ± 0.12 0.19 ± 0.05 0.79 ± 0.01 0.31 ± 0.26 0.99 ± 0.54 0.12 ± 0.01 0.64 ± 0.04 0.80 ± 0.19
35 linalool 21.91 98 0.18 ± 0.00 nd 0.03 ± 0.01 0.03 ± 0.01 nd nd nd nd 0.18 ± 0.06 nd
36 α-terpineol 29.65 98 0.14 ± 0.03 0.13 ± 0.02 nd nd 0.08 ± 0.02 nd 0.05 ± 0.01 0.02 ± 0.00 0.05 ± 0.01 0.08 ± 0.01
39 citronellol 33.85 80 nd nd 0.07 ± 0.01 0.10 ± 0.02 0.18 ± 0.01 0.18 ± 0.04 0.18 ± 0.03 0.34 ± 0.12 0.27 ± 0.11 0.11 ± 0.00
40 nerol 35.51 98 0.35 ± 0.05 0.07 ± 0.02 0.11 ± 0.04 0.12 ± 0.01 0.11 ± 0.04 0.11 ± 0.02 0.07 ± 0.01 0.32 ± 0.02 0.07 ± 0.03 0.04 ± 0.00
41 geraniol 38.18 98 2.39 ± 0.27 0.58 ± 0.15 0.78 ± 0.11 0.75 ± 0.08 0.63 ± 0.05 0.56 ± 0.14 0.42 ± 0.11 1.25 ± 0.13 0.54 ± 0.03 0.45 ± 0.08
Compounds RT MQ (%) GAL.E15 GIR.E31 GIR.E36 GOR.E25 MES.E30 MTN.E20 MTN.E29 PAM.H24 SAB.E35
5 benzaldehyde 20.11 95 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00
8 benzyl alcohol 39.32 98 0.31 ± 0.04 0.20 ± 0.04 0.20 ± 0.02 0.26 ± 0.04 0.35 ± 0.03 0.50 ± 0.13 0.50 ± 0.16 0.38 ± 0.05 1.18 ± 0.10
9 2-phenylethanol 41.03 98 0.04 ± 0.01 0.09 ± 0.01 0.10 ± 0.01 0.06 ± 0.00 0.10 ± 0.00 0.10 ± 0.03 0.10 ± 0.04 0.09 ± 0.00 0.14 ± 0.01
10 eugenol 53.55 98 1.18 ± 0.17 nd nd 1.85 ± 0.53 nd nd nd nd 1.28 ± 0.31
11 4-vinylguaicol 54.88 95 0.23 ± 0.05 0.22 ± 0.08 0.13 ± 0.01 0.35 ± 0.06 0.28 ± 0.05 0.25 ± 0.07 0.23 ± 0.07 0.18 ± 0.02 0.41 ± 0.09
12 4-vinylphenol 62.52 95 0.55 ± 0.16 0.27 ± 0.06 0.27 ± 0.05 0.22 ± 0.11 0.35 ± 0.21 0.22 ± 0.14 0.20 ± 0.07 0.28 ± 0.07 0.30 ± 0.07
35 linalool 21.91 98 nd 1.53 ± 0.33 3.74 ± 0.73 nd nd 0.25 ± 0.00 0.24 ± 0.00 0.37 ± 0.08 nd
36 α-terpineol 29.65 98 0.02 ± 0.01 0.24 ± 0.02 0.31 ± 0.05 0.34 ± 0.04 0.14 ± 0.02 0.06 ± 0.02 0.06 ± 0.01 0.21 ± 0.03 nd
39 citronellol 33.85 80 nd 0.21 ± 0.03 0.11 ± 0.00 nd nd nd nd nd nd
40 nerol 35.51 98 0.07 ± 0.01 0.09 ± 0.03 0.10 ± 0.01 0.07 ± 0.01 0.13 ± 0.04 0.09 ± 0.03 0.10 ± 0.03 0.16 ± 0.04 0.06 ± 0.00
41 geraniol 38.18 98 0.23 ± 0.07 1.07 ± 0.10 1.69 ± 0.21 0.39 ± 0.10 1.57 ± 0.20 0.55 ± 0.15 0.53 ± 0.05 2.04 ± 0.23 0.37 ± 0.01
Appendix III
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Table 4. Odour active value (OAV) in white and rose varieties and standard deviations. Only OAV > 0.20 are shown (data are the average of the tree replicate samples; Keys in Chapter 6; Table 1). RT: Retention time; MQ (%): Match quality (%); nd: not detected
Compounds RT MQ (%) ARG.E34 BEB.E14 BEB.E32 BEB.E39 BEB.E43 BEB.O16 BEB.O17 BEB.O44 BER.E19
5 benzaldehyde 20.11 95 0.01 ± 0.00 0.01 ± 0.01 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.02 ± 0.01 0.01 ± 0.00 nd
8 benzyl alcohol 39.32 98 0.31 ± 0.01 0.33 ± 0.05 0.49 ± 0.12 0.35 ± 0.03 0.19 ± 0.02 0.45 ± 0.04 0.43 ± 0.04 0.29 ± 0.04 0.34 ± 0.11
9 2-phenylethanol 41.03 98 0.13 ± 0.01 0.16 ± 0.02 0.19 ± 0.04 0.15 ± 0.01 0.19 ± 0.02 0.18 ± 0.02 0.18 ± 0.03 0.14 ± 0.01 0.20 ± 0.04
10 eugenol 53.55 98 1.14 ± 0.25 0.61 ± 0.11 1.30 ± 0.35 1.06 ± 0.35 0.53 ± 0.17 1.07 ± 0.21 1.43 ± 0.50 0.80 ± 0.19 1.14 ± 0.37
11 4-vinylguaicol 54.88 95 0.26 ± 0.03 0.25 ± 0.06 0.20 ± 0.02 0.24 ± 0.01 0.23 ± 0.04 0.29 ± 0.06 0.34 ± 0.11 0.33 ± 0.06 0.28 ± 0.01
12 4-vinylphenol 62.52 95 0.08 ± 0.01 0.10 ± 0.03 0.05 ± 0.01 0.04 ± 0.00 0.07 ± 0.01 0.07 ± 0.02 0.06 ± 0.02 0.11 ± 0.05 0.06 ± 0.01
35 linalool 21.91 98 nd 1.02 ± 0.33 0.52 ± 0.11 0.47 ± 0.15 nd 0.29 ± 0.07 0.72 ± 0.25 0.46 ± 0.11 0.74 ± 0.19
36 α-terpineol 29.65 98 nd 0.10 ± 0.03 0.12 ± 0.06 0.06 ± 0.01 0.10 ± 0.01 0.11 ± 0.02 0.07 ± 0.00 0.06 ± 0.01 0.10 ± 0.02
39 citronellol 33.85 80 0.17 ± 0.03 nd nd nd nd nd nd 0.37 ± 0.11 nd
40 nerol 35.51 98 0.14 ± 0.03 0.23 ± 0.05 0.16 ± 0.07 0.12 ± 0.00 0.08 ± 0.02 0.13 ± 0.03 0.17 ± 0.04 0.18 ± 0.04 0.17 ± 0.04
41 geraniol 38.18 98 1.70 ± 0.18 1.14 ± 0.05 1.04 ± 0.33 0.85 ± 0.07 0.70 ± 0.09 0.74 ± 0.04 0.76 ± 0.17 1.12 ± 0.13 0.96 ± 0.18
Compounds RT MQ (%) PEN.SC QUI.E33 QUI.E38 VIN.E45
5 benzaldehyde 20.11 95 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.29 ± 0.01
8 benzyl alcohol 39.32 98 0.28 ± 0.01 0.39 ± 0.06 0.50 ± 0.09 0.54 ± 0.02
9 2-phenylethanol 41.03 98 0.08 ± 0.00 0.14 ± 0.01 0.13 ± 0.00 0.13 ± 0.01
10 eugenol 53.55 98 0.77 ± 0.02 1.00 ± 0.23 0.86 ± 0.18 2.01 ± 0.34
11 4-vinylguaicol 54.88 95 0.43 ± 0.02 0.48 ± 0.12 0.42 ± 0.06 0.36 ± 0.05
12 4-vinylphenol 62.52 95 0.03 ± 0.01 0.10 ± 0.02 0.12 ± 0.02 0.06 ± 0.01
35 linalool 21.91 98 nd nd nd nd
36 α-terpineol 29.65 98 0.03 ± 0.00 0.04 ± 0.01 0.02 ± 0.00 nd
39 citronellol 33.85 80 nd 0.16 ± 0.00 0.20 ± 0.00 nd
40 nerol 35.51 98 0.07 ± 0.01 0.06 ± 0.01 0.05 ± 0.02 0.08 ± 0.02
41 geraniol 38.18 98 0.40 ± 0.01 0.36 ± 0.05 0.16 ± 0.01 0.60 ± 0.03
Appendix III
- 177 -
Figure 1. Typical chromatogram of a red variety
Figure 2. Typical chromatogram of a white variety
Appendix IV: Resumen en castellano
Appendix IV
- 179 -
Resumen Capítulo 2: Evidencias de la pérdida de diversidad de vides cultivadas: el ejemplo de las Islas Baleares (España)
La pérdida de variedades de vid en todo el mundo ha producido una importante erosión genética
del material de base. Este problema es más notable en áreas aisladas por su particular situación,
caracterizada por especímenes únicos, como es el caso de las Islas Baleares (España).
El objetivo de este trabajo fue cuantificar la pérdida de variedades de vid desde el siglo XVII
hasta la actualidad, y para alcanzarlo, fue necesaria una minuciosa investigación para poder
identificar los nombres de las variedades de vid encontrados en la bibliografía antigua. Con el fin
de comparar las descripciones ampelográficas antiguas con las variedades conservadas en la
actualidad, se utilizaron las descripciones ampelográficas encontradas en la literatura consultada,
así como la información disponible en dos bancos de germoplasma. En este trabajo se discuten
las posibles causas que han cambiado la viticultura balear, tales como diferentes enfermedades
(oídio, pulgón o la plaga filoxérica), la influencia de las Denominaciones de Origen sobre la
evolución del número de cultivares y su superficie de cultivo o la entrada de variedades foráneas.
Más del 75% de las variedades encontradas en la bibliografía antigua pudieron ser
identificadas. Uno de los resultados más interesantes fue la gran diversidad de variedades de vid
encontradas en las Islas Baleares a pesar de poseer un área geográfica reducida, lo que
convierte a estas islas en un importante reducto para diferentes variedades. Así mismo, y
contrariamente a lo que se pensaba, el mayor cambio que ha sufrido la viticultura balear se ha
datado antes de que la plaga filoxérica entrara en las islas. Las islas fueron divididas en
diferentes áreas de viticultura, cada una de ellas con una variedad de referencia. La metodología
utilizada en este trabajo ha resultado útil para cuantificar la pérdida de diversidad genética en vid.
Este trabajo ha permitido concluir que, desafortunadamente, algunos de los antiguos cultivares se
han se han perdido o se encuentran en peligro de extinción en la actualidad, habiéndose
cuantificado una importante pérdida de diversidad genética, alrededor del 50%, a lo largo del
periodo estudiado.
Palabras clave: Conservación genética, evolución del cultivo, variedades minoritarias, Vitis
vinifera
Appendix IV
- 180 -
Resumen Capítulo 3: Ampelografía: una vieja técnica con usos futuros,
el caso de variedades minoritarias de vid (Vitis vinifera L.) de las Islas
Baleares
El objetivo de este trabajo fue evaluar las descripciones ampelográficas, análisis genético,
caracterización agronómica, variables de mosto y fenología de 27 accesiones minoritarias de vid
procedentes de las Islas Baleares (España). También se ha estudiado la influencia de un
fenómeno climático ocasional (granizada) sobre las variables agronómicas estudiadas y sobre las
descripciones ampelográficas realizadas. Así mismo, se ha evaluado la influencia de la
experiencia de los ampelógrafos sobre las descripciones ampelográficas. Las accesiones de vid
fueron analizadas utilizando 58 descriptores OIV, tanto cualitativos como cuantitativos, y seis
marcadores moleculares (nuSSR).
Nuestros resultados muestran que la ampelografía es una buena técnica preliminar para
clarificar la identidad del material vegetal, lo cual se ha confirmando también mediante
marcadores moleculares. El color de la hoja joven (OIV-051), la jugosidad de la pulpa (OIV-232) y
la firmeza de la pulpa (OIV-235) han sido los caracteres más difíciles de distinguir por los
ampelógrafos. A pesar de la gran similitud encontrada entre las variedades estudiadas, hubo
ciertos caracteres clave para la identificación de estas variedades (OIV-225, OIV-084, OIV-053,
OIV-004). Además, un fenómeno climático puntual (granizada) influenció sobre las descripciones
ampelográficas, los parámetros agronómicos y la fenología.
La caracterización morfológica y molecular de las 27 accesiones de vid recolectadas en las
Islas Baleares ha permitido validar el método de descripciones ampelográficas agrupándolas en
17 variedades de vid diferentes. El análisis genético ha mostrado que Beba blanca puede ser una
posible mutación somática de Beba roja. Finalmente, se ha observado que la granizada
incrementó el periodo vegetativo de las vides, afectando especialmente a la hoja madura, al
racimo, a las características agronómicas y a la composición del mosto.
En este trabajo se han caracterizado por primera vez los perfiles ampelográficos y moleculares
de estas variedades de vid minoritarias, mostrando el potencial y el interés de éstas y sugiriendo
que su utilización podría ser importante para los viticultores.
Palabras clave: Morfología, variedad de vid, caracterización agronómica, descriptores, influencia
de la granizada
Appendix IV
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Resumen Capítulo 4: Variedades de vid (Vitis vinifera L.) de las Islas
Baleares: caracterización genética y relaciones con la Península Ibérica
y la cuenca Mediterránea
Se ha realizado la identificación de 66 accesiones de Vitis vinifera L. a través de descripciones
ampelográficas, un grupo de 20 marcadores microsatélites nucleares (nuSSR), cinco
microsatélites cloroplásticos (cSSR), así como mediante referencias históricas. El material vegetal
incluye vides mayoritarias y minoritarias en riesgo de extinción recolectadas en las Islas Baleares
y ahora conservadas en dos bancos de germoplasma españoles.
Los resultados de este trabajo señalan que las 66 muestras analizadas se corresponden con
32 genotipos diferentes, entre los que se encuentran algunos genotipos únicos, tres de los cuales
son desconocidos. Se han descubierto diferentes sinonimias y homonimias en la cuenca
Mediterránea, indicando que la dispersión de algunas variedades está relacionada con
movimientos humanos históricos y migraciones ocurridas en tres periodos, (1) alrededor del siglo
VII relacionado con la expansión del Islam, (2) entre los siglos XIII-XV y (3) en el siglo XIX
relacionado con la crisis filoxérica.
Se han identificado algunas relaciones de parentesco entre las variedades estudiadas, siendo
Callet Cas Concos una variedad clave en muchos cruzamientos, lo que confirma el alto valor que
presentan las variedades desconocidas en los análisis de parentesco. Los diferentes métodos de
agrupación utilizados han confirmado la existencia de dos reservas genéticas.
Palabras clave: microsatélites nucleares, clorotipo, variedades minoritarias, estructura genética,
análisis de parentesco
Resumen Capítulo 5: Evaluación de la susceptibilidad al oídio
(Erysiphe necator) en variedades de Vitis vinifera
Se ha evaluado la susceptibilidad al oídio (Erysiphe necator Schwein.) en 159 variedades de vid
tanto extranjeras como autóctonas que se cultivan en España. También se ha estudiado la
relación entre caracteres morfológicos y su susceptibilidad a la enfermedad. La infección de las
variedades de vid se ha estudiado en condiciones naturales tanto en hoja como en racimos. Se
ha encontrado que 35 variedades fueron muy susceptibles a la enfermedad (de muy baja a baja
resistencia en racimos), mientras que otras 83 variedades mostraron baja susceptibilidad (de alta
a muy alta resistencia en racimos). Los resultados proporcionan una información útil en la
selección de variedades menos susceptibles al oídio tanto para viticultores como para
mejoradores.
Palabras clave: Erysiphe necator, morfología, oídio, susceptibilidad, Vitis vinifera
Appendix IV
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Resumen Capítulo 6: Caracterización aromática y potencial enológico
de 21 variedades minoritarias de vid (Vitis vinifera L.)
La homogenización del mercado internacional del vino está dando lugar a un gradual
empobrecimiento de la reserva genética de los viñedos. De hecho, en diferentes Denominaciones
de Origen españolas las variedades de vid internacionales están frecuentemente reemplazando a
las variedades locales, lo que provoca que las variedades minoritarias, perfectamente adaptadas
a las condiciones medioambientales, estén en la actualidad en riesgo de extinción. El estudio de
variedades minoritarias podría proporcionar información útil para satisfacer la demanda de
nuevos productos vitícolas. Este trabajo pretende cubrir la falta de información relativa al
potencial aromático de variedades minoritarias, por lo que se ha considerado el estudio de
compuestos volátiles glicosilados y la evaluación de la influencia de diferentes variables sobre la
composición aromática de las uvas. Se han identificado y cuantificado 51 compuestos
glicosilados. Para poder identificar los compuestos aromáticos más potentes de las variedades
estudiadas se han utilizado “Odour Activity Values” (OAVs). En la evaluación del OAV, las series
odorantes más importantes fueron floral, especiada y fenólica. Los resultados revelan diferencias
para los compuestos glicosilados teniendo en cuenta la variedad, el color de la baya, el clon y el
origen de las muestras. Por otra parte, la síntesis de algunos compuestos implicados en estas
diferenciaciones parece tener un componente genético. La caracterización del potencial
aromático de diferentes variedades minoritarias de vid, llevadas a cavo por primera vez, revelan
que algunas de ellas (Argamusa, Gorgollassa and Pampolat girat) podrían representar una
excelente opción para los enólogos y para desarrollar estrategias de diversificación comercial,
además de ser importante para la conservación de estas variedades.
Palabras clave: Aroma, glicósidos, cromatografía de gases, “flavor”
Appendix IV
- 183 -
Resumen Capítulo 7: Caracterización sensorial y factores que influyen
en la calidad de los vinos elaborados a partir de 18 variedades
minoritarias de vid (Vitis vinifera L.)
El mercado enológico está vivo, siempre en busca de nuevas variedades para satisfacer la
demanda de los consumidores. De este modo, las variedades minoritarias podrían ser firmes
candidatas para satisfacer esta demanda emergente, sin embargo, no se conoce el potencial
enológico de la mayoría de estas variedades. Aunque la calidad de los vinos es difícil de evaluar,
los análisis cuantitativos y descriptivos son los métodos más utilizados para la caracterización
sensorial de los vinos. El objetivo de este trabajo fue caracterizar vinos elaborados con
variedades locales utilizando la descripción sensorial. Se analizaron sensorialmente en dos
vendimias vinos elaborados a partir de 18 variedades minoritarias de vid, incluyendo vinos tintos,
blancos y rosados. El análisis de la influencia de la vendimia, de la inclusión de la variedad en
Denominación de Origen y de los parámetros agronómicos sobre los atributos sensoriales
evaluados por 21 expertos catadores de vino, ha sido realizado mediante análisis de la varianza,
análisis de componentes principales y “partial least squares”. Se ha realizado además un mapa
de preferencia de los catadores para identificar las posibilidades de estos vinos.
Este trabajo destaca que el cuestionario utilizado y los expertos fueron eficientes para evaluar
los vinos catados y que el efecto vendimia ha sido más importante que el efecto Denominación de
Origen sobre la caracterización química y sensorial de los vinos. El análisis sensorial realizado
sobre estos vinos ha demostrado una correlación significativa entre los atributos sensoriales y los
parámetros agronómicos evaluados. Los parámetros agronómicos vegetativos y productivos
tienen una influencia inversa sobre los parámetros aromáticos y el sabor de los vinos dado por los
expertos, por lo que el manejo de estos parámetros en campo podría mejorar la calidad de los
vinos. Este estudio también muestra las posibilidades que podrían tener en el mercado enológico
las variedades estudiadas, especialmente aquellas que han sido aceptadas por los expertos
catadores. Las variedades localizadas en la mejor posición del mapa de preferencia de los
catadores deberían ser consideradas por los técnicos debido a su calidad. Algunas de estas
variedades minoritarias han sido incluso mejor posicionadas que los vinos elaborados con
variedades permitidas en Denominaciones de Origen españolas. Esta información podría
despertar un mayor interés sobre variedades minoritarias o autóctonas, siendo clave para su
conservación.
Palabras clave: preferencia de los expertos, variedades locales, análisis sensorial, vendimia,
calidad del vino